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.gitattributes

@@ -95,3 +95,6 @@ assests/6.jpeg filter=lfs diff=lfs merge=lfs -text
 assests/7.jpeg filter=lfs diff=lfs merge=lfs -text
 assests/8.jpeg filter=lfs diff=lfs merge=lfs -text
 assests/9.jpg filter=lfs diff=lfs merge=lfs -text
+static/AA240126014782K_RC03012026.pdf filter=lfs diff=lfs merge=lfs -text
+static/CERTIFICATE[[:space:]]OF[[:space:]]RECOGNITION.pdf filter=lfs diff=lfs merge=lfs -text
+static/new[[:space:]]Print[[:space:]]_[[:space:]]Udyam[[:space:]]Registration[[:space:]]Certificate.pdf filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,34 @@
+# Environment variables - keep this file private!
+.env
+
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+
+# Virtual environment
+venv/
+env/
+ENV/
+
+# Model cache
+model_cache/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Build
+build/
+dist/
+*.egg-info/

Dockerfile

@@ -1,31 +1,26 @@
-FROM python:3.11-slim
-
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    && rm -rf /var/lib/apt/lists/*
-
-# Copy requirements first
-COPY requirements.txt .
-
-# Install Python packages
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy ALL files
-COPY . .
-
-# Create necessary directories
-RUN mkdir -p /app/model_cache /app/data
-
-# Set environment variables
-ENV PYTHONUNBUFFERED=1
-ENV PORT=7860
-ENV SENTENCE_TRANSFORMERS_HOME=/app/model_cache
-
-EXPOSE 7860
-
-# Run app3.py instead of app.py
-CMD ["python", "app3.py"]
+FROM python:3.11-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+RUN mkdir -p data model_cache
+
+ENV FLASK_APP=app.py
+ENV PYTHONUNBUFFERED=1
+ENV SENTENCE_TRANSFORMERS_HOME=/app/model_cache
+
+EXPOSE 5000
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD curl -f http://localhost:5000/health || exit 1
+
+CMD ["python", "app.py"]

README.md

@@ -1,14 +1,99 @@
----
-title: RNGPIT AI Chatbot
-emoji: 🤖
-colorFrom: blue
-colorTo: green
-sdk: docker
-sdk_version: "3.11"
-app_file: app.py
-pinned: false
----
-
-# RNGPIT AI Assistant
-
-An AI-powered chatbot for RNG Patel Institute of Technology using RAG and Groq API.
+# RNGAI-WEB Chatbot
+
+This is an AI-powered chatbot for RNG Patel Institute of Technology (RNGPIT) that uses RAG (Retrieval Augmented Generation) to answer questions about courses, admissions, facilities, placements, and campus life.
+
+## Features
+
+- 🎓 Comprehensive information about RNGPIT
+- 🤖 Powered by Groq AI (LLaMA models)
+- 📚 RAG-based knowledge retrieval
+- 🎨 Beautiful, modern UI with dark/light theme
+- 📊 Admin dashboard with analytics
+- 🐳 Docker support for easy deployment
+
+## Setup
+
+### 1. Clone and Install
+
+```bash
+cd RNGAI-WEB
+pip install -r requirements.txt
+```
+
+### 2. Configure Environment Variables
+
+Copy the example environment file:
+
+```bash
+copy .env.example .env
+```
+
+Then edit `.env` and add your credentials:
+
+```env
+GROQ_API_KEY=your_groq_api_key_here
+SUPABASE_URL=your_supabase_url_here
+SUPABASE_KEY=your_supabase_key_here
+```
+
+**Get your credentials:**
+- **Groq API**: https://console.groq.com
+- **Supabase**: https://supabase.com
+
+### 3. Run the Application
+
+**Local Development:**
+
+```bash
+python app.py
+```
+
+**Using Docker:**
+
+```bash
+docker-compose up -d
+```
+
+The application will be available at: `http://localhost:5000`
+
+## Project Structure
+
+```
+RNGAI-WEB/
+├── app.py                 # Main Flask application
+├── requirements.txt       # Python dependencies
+├── .env                   # Environment variables (not in git)
+├── .env.example          # Environment template
+├── Dockerfile            # Docker configuration
+├── docker-compose.yml    # Docker Compose config
+├── templates/
+│   └── index.html        # Main chat interface
+├── static/
+│   └── RNGPIT.png       # College logo
+└── data/
+    └── link17.txt       # Knowledge base
+```
+
+## Environment Variables
+
+| Variable | Description | Required |
+|----------|-------------|----------|
+| `GROQ_API_KEY` | API key from Groq Console | Yes |
+| `SUPABASE_URL` | Your Supabase project URL | Yes |
+| `SUPABASE_KEY` | Your Supabase API key | Yes |
+
+## Tech Stack
+
+- **Backend**: Flask, Python 3.11
+- **AI**: Groq (LLaMA 3.3), Sentence Transformers
+- **Vector DB**: ChromaDB
+- **Database**: Supabase
+- **Frontend**: HTML, CSS, JavaScript with Three.js
+
+## Security
+
+⚠️ **Important**: Never commit your `.env` file to version control. The `.env` file contains sensitive credentials and should be kept private.
+
+## License
+
+Private project for RNG Patel Institute of Technology

app.py

@@ -0,0 +1,1120 @@
+from flask import Flask, render_template, request, jsonify, session, redirect, url_for, copy_current_request_context
+from flask_cors import CORS
+import os
+import sys
+import json
+from sentence_transformers import SentenceTransformer
+import chromadb
+from chromadb.config import Settings
+import re
+import numpy as np
+from typing import List, Dict, Tuple, Optional
+from supabase import create_client, Client
+from datetime import datetime
+import uuid
+import time
+from functools import wraps, lru_cache
+import threading
+from queue import Queue
+import gc
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# OpenAI SDK for NVIDIA API (DeepSeek model)
+try:
+    from openai import OpenAI
+    NVIDIA_AVAILABLE = True
+except ImportError:
+    NVIDIA_AVAILABLE = False
+    print("[WARN] openai package not installed. Run: pip install openai")
+
+# Fix Windows console encoding
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+if sys.platform == 'win32':
+    try:
+        sys.stdout.reconfigure(encoding='utf-8')
+    except:
+        pass
+
+app = Flask(__name__)
+CORS(app)
+app.secret_key = 'rngai_secret_key_change_in_production_2025'
+
+# ============================================
+# CONFIGURATION
+# ============================================
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+TEXT_FILE_PATH = os.path.join(SCRIPT_DIR, "data", "link17.txt")
+MODEL_CACHE_DIR = os.path.join(SCRIPT_DIR, "model_cache")
+
+# ============================================
+# NVIDIA MODEL CONFIGURATION (NVIDIA API)
+# ============================================
+NVIDIA_MODEL_REGISTRY = {
+    "abacusai/dracarys-llama-3.1-70b-instruct": {
+        "name": "Dracarys Llama 3.1 70B",
+        "model_id": "abacusai/dracarys-llama-3.1-70b-instruct",
+        "context_length": 128000,
+        "max_new_tokens": 4096,
+        "description": "Dracarys Llama 3.1 - Powerful 70B Instruct Model",
+    },
+}
+
+DEFAULT_NVIDIA_MODEL = "abacusai/dracarys-llama-3.1-70b-instruct"
+
+# Cost tracking (NVIDIA API pricing)
+NVIDIA_COST_PER_1M_INPUT = 0.0  # Check NVIDIA pricing
+NVIDIA_COST_PER_1M_OUTPUT = 0.0  # Check NVIDIA pricing
+
+# ============================================
+# NVIDIA CONFIGURATION
+# ============================================
+NVIDIA_API_KEY = os.environ.get('NVIDIA_API_KEY')
+NVIDIA_API_KEY_2 = os.environ.get('NVIDIA_API_KEY_2')  # Backup key
+NVIDIA_BASE_URL = os.environ.get('NVIDIA_BASE_URL', 'https://integrate.api.nvidia.com/v1')
+nvidia_client = None
+current_nvidia_model = DEFAULT_NVIDIA_MODEL
+
+# Initialize NVIDIA OpenAI client
+if NVIDIA_API_KEY and NVIDIA_AVAILABLE:
+    nvidia_client = OpenAI(
+        base_url=NVIDIA_BASE_URL,
+        api_key=NVIDIA_API_KEY
+    )
+    print(f"[OK] NVIDIA API client initialized (Key 1)")
+    if NVIDIA_API_KEY_2:
+        print(f"[OK] Backup NVIDIA key configured")
+else:
+    if not NVIDIA_API_KEY:
+        print("[WARN] NVIDIA_API_KEY not set in environment")
+# Supabase Configuration (loaded from .env)
+SUPABASE_URL = os.environ.get('SUPABASE_URL')
+SUPABASE_KEY = os.environ.get('SUPABASE_KEY')
+
+if not SUPABASE_URL:
+    print("[WARN] SUPABASE_URL is missing from environment variables!")
+if not SUPABASE_KEY:
+    print("[WARN] SUPABASE_KEY is missing from environment variables!")
+
+try:
+    if SUPABASE_URL and SUPABASE_KEY:
+        supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
+        print("[OK] Supabase connected successfully")
+    else:
+        print("[WARN] Skipping Supabase connection due to missing config")
+        supabase = None
+except Exception as e:
+    print(f"[WARN] Could not connect to Supabase: {e}")
+    supabase = None
+
+# ============================================
+# GLOBAL VARIABLES
+# ============================================
+embedding_model = None
+collection = None
+chroma_client = None
+debug_mode = False
+
+# OPTIMIZATION: Cache for embeddings
+EMBEDDING_CACHE_SIZE = 200
+embedding_cache = {}
+
+# ============================================
+# EMBEDDING MODEL (Same as original)
+# ============================================
+
+def load_embedding_model():
+    """Load embedding model with optimizations"""
+    global embedding_model
+
+    if embedding_model is not None:
+        return embedding_model
+
+    print("[INFO] Loading embedding model (optimized)...")
+    embedding_model = SentenceTransformer('mixedbread-ai/mxbai-embed-large-v1')
+
+    # Check if GPU is available for embeddings
+    try:
+        import torch
+        if torch.cuda.is_available():
+            embedding_model = embedding_model.to('cuda')
+            print("[PERF] Embedding model on GPU")
+    except:
+        print("[INFO] Embedding model on CPU")
+
+    print("[OK] Embedding model loaded!")
+    return embedding_model
+
+@lru_cache(maxsize=EMBEDDING_CACHE_SIZE)
+def get_cached_embedding(text: str) -> np.ndarray:
+    """Cache embeddings for frequently asked questions"""
+    global embedding_model
+    if embedding_model is None:
+        return None
+
+    instruction = "Represent this sentence for searching relevant passages: "
+    embedding = embedding_model.encode(
+        [instruction + text], 
+        normalize_embeddings=True,
+        show_progress_bar=False,
+        batch_size=1,
+        convert_to_numpy=True
+    )[0]
+    return embedding
+
+# ============================================
+# AUTH HELPERS
+# ============================================
+
+def login_required(f):
+    @wraps(f)
+    def decorated_function(*args, **kwargs):
+        if 'admin_logged_in' not in session or not session['admin_logged_in']:
+            return redirect(url_for('admin_login'))
+        return f(*args, **kwargs)
+    return decorated_function
+
+def get_or_create_session():
+    if 'chat_session_id' not in session:
+        session['chat_session_id'] = str(uuid.uuid4())
+
+        if supabase:
+            try:
+                supabase.table('chat_sessions').insert({
+                    'session_id': session['chat_session_id'],
+                    'ip_address': request.remote_addr,
+                    'user_agent': request.user_agent.string[:500] if request.user_agent.string else None,
+                    'started_at': datetime.utcnow().isoformat()
+                }).execute()
+            except Exception as e:
+                print(f"[WARN] Could not create session: {e}")
+
+    return session['chat_session_id']
+
+def save_chat_to_supabase(session_id: str, user_question: str, ai_response: str, 
+                          response_time_ms: int, input_tokens: int = 0, output_tokens: int = 0):
+    """Save chat synchronously but non-blocking"""
+    if not supabase:
+        return
+
+    try:
+        result = supabase.table('chat_sessions').select('id').eq('session_id', session_id).execute()
+
+        total_cost = 0.0  # Groq is free tier
+
+        if result.data:
+            session_uuid = result.data[0]['id']
+
+            supabase.table('chat_messages').insert({
+                'session_id': session_uuid,
+                'user_question': user_question,
+                'ai_response': ai_response[:5000],
+                'response_time_ms': response_time_ms,
+                'input_tokens': input_tokens,
+                'output_tokens': output_tokens,
+                'total_cost': total_cost,
+                'created_at': datetime.utcnow().isoformat()
+            }).execute()
+            print(f"[OK] Chat saved (tokens: in={input_tokens}, out={output_tokens})")
+    except Exception as e:
+        print(f"[WARN] Failed to save chat: {e}")
+
+# ============================================
+# TEXT PROCESSING (Same as original)
+# ============================================
+
+@lru_cache(maxsize=1)
+def load_and_process_data(file_path: str) -> List[Dict]:
+    """Cached data loading"""
+    try:
+        print(f"[INFO] Loading data from: {file_path}")
+
+        if not os.path.exists(file_path):
+            print(f"[ERROR] File not found: {file_path}")
+            return []
+
+        with open(file_path, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        print(f"[OK] Loaded {len(content):,} characters")
+
+        chunks = create_chunks(content)
+        print(f"[OK] Created {len(chunks)} chunks")
+
+        return chunks
+
+    except Exception as e:
+        print(f"[ERROR] Error loading file: {e}")
+        return []
+
+def clean_text(text: str) -> str:
+    """Optimized text cleaning"""
+    text = text.replace('\t', '    ')
+    text = re.sub(r'[^\w\s.,!?;:()\-\'\"@/&|\[\]#]+', ' ', text)
+    text = re.sub(r' +', ' ', text)
+    text = re.sub(r'\n{3,}', '\n\n', text)
+    return text.strip()
+
+def create_chunks(text: str, chunk_size: int = 1000, overlap: int = 200) -> List[Dict]:
+    """Balanced chunking - original parameters for quality"""
+    sections = re.split(r'\n\s*\n|\n-{4,}\n', text)
+
+    chunks = []
+    chunk_id = 0
+    current_chunk = []
+    current_length = 0
+
+    for section in sections:
+        section = section.strip()
+        if not section:
+            continue
+
+        section_lines = section.split('\n')
+
+        for line in section_lines:
+            line = line.strip()
+            if not line:
+                continue
+
+            line_words = len(line.split())
+
+            if current_length + line_words > chunk_size and current_chunk:
+                chunk_text = '\n'.join(current_chunk)
+                chunks.append({
+                    'text': chunk_text,
+                    'id': chunk_id,
+                })
+                chunk_id += 1
+
+                overlap_buffer = []
+                overlap_len = 0
+                for prev_line in reversed(current_chunk):
+                    prev_len = len(prev_line.split())
+                    if overlap_len + prev_len > overlap:
+                        break
+                    overlap_buffer.insert(0, prev_line)
+                    overlap_len += prev_len
+
+                current_chunk = overlap_buffer
+                current_length = overlap_len
+
+            current_chunk.append(line)
+            current_length += line_words
+
+    if current_chunk:
+        chunks.append({
+            'text': '\n'.join(current_chunk),
+            'id': chunk_id,
+        })
+
+    return chunks
+
+# ============================================
+# RAG INITIALIZATION (Same as original)
+# ============================================
+
+def initialize_rag():
+    """Initialize RAG with optimizations"""
+    global collection, embedding_model, chroma_client
+
+    print("\n" + "="*60)
+    print("INITIALIZING RAG SYSTEM")
+    print("="*60)
+
+    embedding_model = load_embedding_model()
+
+    print("[INFO] Initializing vector database...")
+    chroma_client = chromadb.Client(Settings(
+        anonymized_telemetry=False,
+        is_persistent=False
+    ))
+
+    collection = chroma_client.get_or_create_collection(
+        name="rngpit_knowledge",
+        metadata={"description": "RNG Patel Institute Knowledge Base"}
+    )
+
+    chunks = load_and_process_data(TEXT_FILE_PATH)
+
+    if not chunks:
+        print("[WARN] No data loaded!")
+        return
+
+    print("[INFO] Generating embeddings (batched)...")
+    chunk_texts = [chunk['text'] for chunk in chunks]
+
+    # Optimized batch encoding
+    embeddings = embedding_model.encode(
+        chunk_texts,
+        show_progress_bar=True,
+        batch_size=64,  # Balanced batch size
+        normalize_embeddings=True,
+        convert_to_numpy=True
+    )
+
+    print("[INFO] Adding to vector database...")
+    collection.add(
+        embeddings=embeddings.tolist(),
+        documents=chunk_texts,
+        ids=[f"chunk_{i}" for i in range(len(chunks))]
+    )
+
+    print("\n" + "="*60)
+    print(f"RAG READY! ({len(chunks)} chunks)")
+    print("="*60 + "\n")
+
+def retrieve_context(query: str, top_k: int = 5) -> List[str]:
+    """Optimized retrieval with caching - BALANCED: 5 chunks for quality"""
+    global collection, embedding_model
+
+    if collection is None or embedding_model is None:
+        return []
+
+    try:
+        # Use cached embedding
+        query_embedding = get_cached_embedding(query)
+        if query_embedding is None:
+            return []
+
+        results = collection.query(
+            query_embeddings=[query_embedding.tolist()],
+            n_results=top_k
+        )
+
+        if results and results['documents']:
+            return results['documents'][0]
+        return []
+
+    except Exception as e:
+        print(f"[ERROR] Retrieval error: {e}")
+        return []
+
+# ============================================
+# NVIDIA RESPONSE GENERATION (OneAI SDK)
+# ============================================
+
+def generate_response_nvidia(query: str, context_chunks: List[str]) -> Dict:
+    """Generate response using NVIDIA API with Dracarys Llama 3.1 model"""
+    global NVIDIA_API_KEY, nvidia_client, current_nvidia_model
+
+    if not context_chunks:
+        return {
+            'text': "I don't have specific information about that. Could you ask me something else about RNG Patel Institute?",
+            'input_tokens': 0,
+            'output_tokens': 0,
+            'model_used': 'none'
+        }
+
+    if not NVIDIA_AVAILABLE:
+        return {
+            'text': "OpenAI SDK not installed. Run: pip install openai",
+            'input_tokens': 0,
+            'output_tokens': 0,
+            'model_used': 'error'
+        }
+
+    if not NVIDIA_API_KEY:
+        return {
+            'text': "NVIDIA API key not configured. Please set NVIDIA_API_KEY environment variable.",
+            'input_tokens': 0,
+            'output_tokens': 0,
+            'model_used': 'error'
+        }
+
+    try:
+        # Initialize NVIDIA client if needed
+        if nvidia_client is None:
+            nvidia_client = OpenAI(
+                base_url=NVIDIA_BASE_URL,
+                api_key=NVIDIA_API_KEY
+            )
+            print("[OK] Initialized NVIDIA client")
+
+        # Build context
+        context_parts = [chunk.strip() for chunk in context_chunks[:10] if chunk.strip()]
+        context = "\n\n".join(context_parts)[:40000]
+
+        # System prompt
+        system_prompt = """You are a friendly and knowledgeable student ambassador for RNGPIT (R.N.G. Patel Institute of Technology). Your goal is to help students and visitors by answering their questions warmly and directly.
+
+**ABOUT THIS AI (Important):**
+When asked about "who made this AI", "who created you", "who built you", or similar questions about the creators/developers, respond with:
+
+"I was built by **Team InnoCrew**, a talented group of students from RNGPIT:
+
+- **Shis Tushar Maheta** (Lead AI Engineer) - B.Tech Computer Science, Class of 2025
+- **Zuveriya Meman** -B.Voc Software development, Class of 2025
+- **Karan Chaudhary** - B.Voc Software Development, Class of 2023
+- **Sem Surti** - B.Voc Software Development, Class of 2023
+- **Shreyansh Vasava** - B.Voc Software Development, Class of 2023
+
+Team InnoCrew developed this AI assistant to help students and visitors learn more about RNG Patel Institute of Technology!"
+
+EXACT FACULTY DETIALS:
+
+#### **1. Information Technology (IT) Department Faculty**
+
+| Name | Designation | Education | Exp. | Email |
+| --- | --- | --- | --- | --- |
+| **Prof. Vivek C. Joshi** | I/C HOD & Asst. Prof | M.Tech (CSE), Ph.D. (Pursuing) | 13+ Yrs | vcjoshi@rngpit.ac.in |
+| **Prof. Hardi A. Patel** | Assistant Professor | M.E. (CSE) | 6+ Yrs | hapatel@rngpit.ac.in |
+| **Prof. Krina N. Desai** | Assistant Professor | M.E. (CE) | 3+ Yrs | kndesai@rngpit.ac.in |
+| **Prof. Nishtha H. Tandel** | Assistant Professor | M.Tech (IT), GSET Qualified | 4+ Yrs | nhtandel@rngpit.ac.in |
+| **Prof. Bhavisha S. Parmar** | Assistant Professor | M.E. (CE), Ph.D. (Pursuing) | 12+ Yrs | bsparmar@rngpit.ac.in |
+| **Prof. Foram C. Shukla** | Assistant Professor | M.E. (CE) | 1 Yr | fcshukla@rngpit.ac.in |
+| **Prof. Purvaj P. Vaidya** | Assistant Professor | M.Tech (Media Tech - Germany) | 1 Yr | ppvaidya@rngpit.ac.in |
+| **Prof. Monali R. Gandhi** | Assistant Professor | M.E. (CE) | 11+ Yrs | mrgandhi@rngpit.ac.in |
+| **Prof. Ekta R. Bhatia** | Assistant Professor | M.Tech (CSE) | 4 Yrs | erbhatia@rngpit.ac.in |
+| **Prof. Pratik M. Gohil** | Assistant Professor | M.Tech (CE) | 5+ Yrs | pmgohil@rngpit.ac.in |
+| **Prof. Zeel R. Bhatt** | Assistant Professor | M.Tech (CE) | 3 Mos | zrbhatt@rngpit.ac.in |
+| **Prof. Pooja D. Patel** | Assistant Professor | M.E. (CE) | - | pdpatel@rngpit.ac.in |
+| **Prof. Ayushi H. Gandhi** | Assistant Professor | M.E. (CE) | 4+ Yrs | ahgandhi@rngpit.ac.in |
+| **Prof. Rinisha S. Patel** | Assistant Professor | M.E. (CE) | - | rspatel@rngpit.ac.in |
+
+#### **2. Mechanical Engineering Department Faculty**
+
+| Name | Designation | Education | Email |
+| --- | --- | --- | --- |
+| **Dr. Kanti B. Rathod** | HOD & Assoc. Professor | Ph.D. (Mechanical) | kbrathod@rngpit.ac.in |
+| **Mr. Hardik B. Nayak** | Assistant Professor | Ph.D. (Pursuing) | hbnayak@rngpit.ac.in |
+| **Mr. Niravsinh B. Rathod** | Assistant Professor | Ph.D. (Pursuing) | nbrathod@rngpit.ac.in |
+| **Mr. Gaurang K. Champaneri** | Assistant Professor | M.Tech (CIM) | gkchampaneri@rngpit.ac.in |
+| **Mr. Chirag K. Balar** | Assistant Professor | M.Tech (Mechanical) | ckbalar@rngpit.ac.in |
+| **Mr. Dharmin M. Patel** | Assistant Professor | Ph.D. (Pursuing) | dmpatel@rngpit.ac.in |
+| **Mr. Nevilkumar M. Patel** | Assistant Professor | M.E. (Machine Design) | nmpatel@rngpit.ac.in |
+| **Mr. Yatin H. Chauhan** | Assistant Professor | M.Tech (ME) | yhchauhan@rngpit.ac.in |
+| **Mr. Vikramkumar A. Mistry** | Assistant Professor | M.E. (IC Engine & Automobile) | vamistry@rngpit.ac.in |
+| **Mr. Sushant K. Merai** | Assistant Professor | Ph.D. (Pursuing) | skmerai@rngpit.ac.in |
+| **Mr. Sapan H. Joshi** | Assistant Professor | M.Tech (Thermal System) | shjoshi@rngpit.ac.in |
+| **Dr. Ankursinh P. Solanki** | Assistant Professor | Ph.D., M.E. (Thermal) | apsolanki@rngpit.ac.in |
+| **Mr. Nikhil M. Pandya** | Assistant Professor | M.E. (Production) | nmpandya@rngpit.ac.in |
+| **Mr. Mehul P. Patel** | Assistant Professor | M.E. (Production) | mppatel@rngpit.ac.in |
+| **Mr. Vikesh B. Patel** | Assistant Professor | M.Tech (CAD/CAM) | patelvikesh1988@gmail.com |
+| **Mr. Shobhit Y. Varshney** | Assistant Professor | M.Tech (Thermal System) | syvarshney@rngpit.ac.in |
+
+#### **3. Civil Engineering Department Faculty**
+
+| Name                         | Designation                 | Qualification                      | Email                                                     | More Info |
+| ---------------------------- | --------------------------- | ---------------------------------- | --------------------------------------------------------- | --------- |
+| Dr. Kamalsinh M. Padhiar     | HOD and Associate Professor | —                                  | [kmpadhiar@rngpit.ac.in](mailto:kmpadhiar@rngpit.ac.in)   | —         |
+| Mr. Gaurav P. Barot          | Assistant Professor         | M.Tech (Structural Engineering)    | [gpbarot@rngpit.ac.in](mailto:gpbarot@rngpit.ac.in)       | —         |
+| Mr. Mohammed Ahmed Qureshi   | Assistant Professor         | M.Tech (Structure), Ph.D. Pursuing | [maqureshi@rngpit.ac.in](mailto:maqureshi@rngpit.ac.in)   | —         |
+| Mr. Nirav P. Desai           | Assistant Professor         | M.E (Transportation)               | [npdesai@rngpit.ac.in](mailto:npdesai@rngpit.ac.in)       | —         |
+| Mr. Sharukh M. Marfani       | Assistant Professor         | M.E (CE)                           | [smmarfani@rngpit.ac.in](mailto:smmarfani@rngpit.ac.in)   | —         |
+| Mr. Hilay N. Prajapati       | Assistant Professor         | M.E (CED)                          | [hnprajapati@ngpit.ac.in](mailto:hnprajapati@ngpit.ac.in) | —         |
+| Mr. Ajay B. Patel            | Assistant Professor         | M.E. (Civil Engg.)                 | [ajaybpatel@rngpit.ac.in](mailto:ajaybpatel@rngpit.ac.in) | —         |
+| Ms. Srushti U. Joshi         | Assistant Professor         | M.E (Civil Engg.)                  | [sujoshi@rngpit.ac.in](mailto:sujoshi@rngpit.ac.in)       | —         |
+| Mr. Priyank H. Patel         | Assistant Professor         | —                                  | [phpatel@rngpit.ac.in](mailto:phpatel@rngpit.ac.in)       | —         |
+| Mr. Atish P. More            | Assistant Professor         | Masters in Environmental Engg.     | [apmore@rngpit.ac.in](mailto:apmore@rngpit.ac.in)         | —         |
+| Ms. Hetvi J. Kania           | Assistant Professor         | M.E. in Environmental Engg.        | [hjkania@rngpit.ac.in](mailto:hjkania@rngpit.ac.in)       | —         |
+| Mr. Pritesh R. Bhandari      | Assistant Professor         | Diploma Civil                      | [prbhandari@rngpit.ac.in](mailto:prbhandari@rngpit.ac.in) | —         |
+| Mr. Viral Jagdishbhai Rathod | Assistant Professor         | M.E. (Production)                  | [vjrathod@rngpit.ac.in](mailto:vjrathod@rngpit.ac.in)     | —         |
+
+#### **GIVE THE DETIALS OF THE DEPARTMENT THAT IS ONLY ASKED DONT PROVIDE ANY OTHER RANDOM DATA**
+
+**STRICT FORMATTING RULES:**
+1. **ALWAYS USE TABLES FOR DATA**: If you are listing more than 3 items (like faculty names, committee members, fees, courses, placements), you **MUST** use a Markdown table. Do not use bullet points for long lists.
+   *Example Table:*
+   | Name | Position | Location |
+   |------|----------|----------|
+   | John Doe | President | Surat |
+
+2. **DIRECT ANSWER**: Answer the question immediately. Do not start with "Okay", "Sure", or "Based on the context".
+
+3. **BEAUTIFUL FORMATTING**: Use **bold** for importance, `code` for emails/numbers, and clear paragraphs.
+
+4. **NO CITATIONS**: Do not say "according to the document". Speak as if you know the facts yourself.
+
+5. **GRACEFUL FALLBACK**: If you don't know, suggest contacting info@rngpit.ac.in."""
+
+        user_prompt = f"""Context Information:
+{context}
+
+User Question: {query}
+
+Answer:"""
+
+        def make_api_call(client_to_use, model_to_use):
+            return client_to_use.chat.completions.create(
+                model=model_to_use,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt}
+                ],
+                temperature=0.5,
+                top_p=0.8,
+                max_tokens=4096,
+                stream=True
+            )
+
+        # Attempt 1: Primary Key
+        try:
+            completion = make_api_call(nvidia_client, current_nvidia_model)
+        except Exception as e:
+            error_msg = str(e)
+            # Check for Rate Limit (429) or Quota issues
+            if ("429" in error_msg or "quota" in error_msg.lower()) and NVIDIA_API_KEY_2 and NVIDIA_API_KEY != NVIDIA_API_KEY_2:
+                print(f"[WARN] detailed error: {error_msg}")
+                print(f"[WARN] Primary API key rate limited using fallback key...")
+                
+                # Switch to backup key
+                NVIDIA_API_KEY = NVIDIA_API_KEY_2
+                nvidia_client = OpenAI(base_url=NVIDIA_BASE_URL, api_key=NVIDIA_API_KEY)
+                
+                # Attempt 2: Backup Key
+                completion = make_api_call(nvidia_client, current_nvidia_model)
+            else:
+                raise e # Re-raise if not a rate limit issue or no backup key
+
+        # Collect streamed response
+        response_text = ""
+        for chunk in completion:
+            if chunk.choices[0].delta.content is not None:
+                response_text += chunk.choices[0].delta.content
+
+        # Estimate token counts
+        input_tokens = int(len((system_prompt + user_prompt).split()) * 1.3)
+        output_tokens = int(len(response_text.split()) * 1.3)
+
+        if not response_text:
+            response_text = "I couldn't generate a response. Please try again."
+
+        print(f"[DEBUG] NVIDIA generated ~{int(output_tokens)} tokens using {current_nvidia_model}")
+
+        return {
+            'text': response_text.strip(),
+            'input_tokens': int(input_tokens),
+            'output_tokens': int(output_tokens),
+            'model_used': current_nvidia_model
+        }
+
+    except Exception as e:
+        error_msg = str(e)
+        print(f"[ERROR] NVIDIA API error: {error_msg}")
+
+        if "API key" in error_msg.lower() or "authentication" in error_msg.lower() or "unauthorized" in error_msg.lower():
+            return {'text': "Invalid NVIDIA API key. Please check your API key.", 'input_tokens': 0, 'output_tokens': 0, 'model_used': 'error'}
+        elif "quota" in error_msg.lower() or "limit" in error_msg.lower() or "rate" in error_msg.lower():
+            return {'text': "API rate limit reached. Please wait a moment and try again.", 'input_tokens': 0, 'output_tokens': 0, 'model_used': 'error'}
+        else:
+            return {'text': f"Error processing request: {error_msg}", 'input_tokens': 0, 'output_tokens': 0, 'model_used': 'error'}
+
+# ============================================
+# MODEL INFO
+# ============================================
+
+def get_model_info():
+    """Get model information"""
+    return {
+        "available_models": {
+            model_id: {
+                "name": config["name"],
+                "description": config["description"],
+                "context_length": config["context_length"],
+                "max_new_tokens": config["max_new_tokens"],
+            }
+            for model_id, config in NVIDIA_MODEL_REGISTRY.items()
+        },
+        "current_model": current_nvidia_model,
+        "is_loaded": bool(NVIDIA_API_KEY),
+        "provider": "nvidia"
+    }
+
+# ============================================
+# ROUTES
+# ============================================
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    global debug_mode
+    try:
+        start_time = time.time()
+
+        data = request.json
+        user_message = data.get('message', '').strip()
+
+        if not user_message:
+            return jsonify({'error': 'No message provided'}), 400
+
+        print(f"\n[CHAT] Query: {user_message}")
+
+        # Get session ID within request context
+        current_session_id = get_or_create_session()
+
+        # Fast retrieval
+        context_chunks = retrieve_context(user_message, top_k=5)
+
+        if not context_chunks:
+            response_text = "I don't have information about that. Try asking about courses, admissions, fees, placements, or facilities at RNGPIT."
+            input_tokens = 0
+            output_tokens = 0
+        else:
+            print(f"[INFO] Found {len(context_chunks)} relevant chunks")
+            print("[INFO] Using NVIDIA API (DeepSeek) for generation...")
+            result = generate_response_nvidia(user_message, context_chunks)
+
+            response_text = result['text']
+            input_tokens = result['input_tokens']
+            output_tokens = result['output_tokens']
+            model_used = result.get('model_used', current_nvidia_model)
+
+
+        response_time_ms = int((time.time() - start_time) * 1000)
+
+        # Save to database (synchronous but fast)
+        save_chat_to_supabase(current_session_id, user_message, response_text, response_time_ms, input_tokens, output_tokens)
+
+        print(f"[OK] Response ({len(response_text)} chars) in {response_time_ms}ms [Provider: NVIDIA DeepSeek]")
+
+        response_data = {
+            'response': response_text,
+            'response_time_ms': response_time_ms
+        }
+
+        if debug_mode:
+            response_data['debug'] = {
+                'enabled': True,
+                'chunks_used': len(context_chunks),
+                'model': model_used if context_chunks else current_nvidia_model,
+                'provider': 'nvidia',
+                'input_tokens': input_tokens,
+                'output_tokens': output_tokens
+            }
+
+        return jsonify(response_data)
+
+    except Exception as e:
+        print(f"[ERROR] Chat error: {e}")
+        import traceback
+        traceback.print_exc()
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/health', methods=['GET'])
+def health():
+    model_info = get_model_info()
+    return jsonify({
+        'status': 'healthy',
+        'chunks_loaded': collection.count() if collection else 0,
+        'current_model': current_nvidia_model,
+        'model_loaded': nvidia_client is not None,
+        'provider': 'nvidia',
+        'debug_mode': debug_mode,
+        'nvidia_configured': bool(NVIDIA_API_KEY)
+    })
+
+# ============================================
+# MODEL MANAGEMENT API
+# ============================================
+
+@app.route('/api/models', methods=['GET'])
+def api_get_models():
+    return jsonify(get_model_info())
+
+@app.route('/api/models/switch', methods=['POST'])
+@login_required
+def api_switch_model():
+    global current_nvidia_model
+
+    data = request.json or {}
+    model_id = data.get('model_id', DEFAULT_NVIDIA_MODEL)
+
+    if model_id not in NVIDIA_MODEL_REGISTRY:
+        return jsonify({
+            'success': False, 
+            'error': f"Model '{model_id}' not found in NVIDIA registry"
+        }), 400
+
+    current_nvidia_model = model_id
+    print(f"[INFO] Switched to NVIDIA model: {model_id}")
+
+    return jsonify({
+        'success': True,
+        'message': f"Switched to {NVIDIA_MODEL_REGISTRY[model_id]['name']}",
+        'model_info': get_model_info()
+    })
+
+@app.route('/api/embeddings/regenerate', methods=['POST'])
+def api_regenerate_embeddings():
+    global collection
+
+    try:
+        if collection is not None:
+            chroma_client.delete_collection("rngpit_knowledge")
+
+        # Clear cache
+        get_cached_embedding.cache_clear()
+        load_and_process_data.cache_clear()
+
+        initialize_rag()
+
+        return jsonify({
+            'success': True,
+            'message': 'Embeddings regenerated',
+            'chunks_loaded': collection.count() if collection else 0
+        })
+    except Exception as e:
+        return jsonify({'success': False, 'error': str(e)}), 500
+
+@app.route('/api/debug/toggle', methods=['POST'])
+def api_toggle_debug():
+    global debug_mode
+    debug_mode = not debug_mode
+    return jsonify({'success': True, 'debug_mode': debug_mode})
+
+@app.route('/api/debug/status', methods=['GET'])
+def api_debug_status():
+    return jsonify({'debug_mode': debug_mode})
+
+@app.route('/api/nvidia-key', methods=['POST'])
+@login_required
+def api_set_nvidia_key():
+    global NVIDIA_API_KEY, nvidia_client
+
+    data = request.json or {}
+    api_key = data.get('api_key', '').strip()
+
+    if not api_key:
+        return jsonify({'success': False, 'error': 'API key required'}), 400
+
+    NVIDIA_API_KEY = api_key
+    nvidia_client = OpenAI(base_url=NVIDIA_BASE_URL, api_key=NVIDIA_API_KEY)  # Reset client with new key
+    print("[INFO] NVIDIA API key updated")
+
+    return jsonify({
+        'success': True,
+        'message': 'NVIDIA API key configured',
+        'key_preview': api_key[:8] + '...' + api_key[-4:] if len(api_key) > 12 else '***'
+    })
+
+@app.route('/api/nvidia-key/status', methods=['GET'])
+@login_required
+def api_nvidia_key_status():
+    if NVIDIA_API_KEY:
+        return jsonify({
+            'configured': True,
+            'key_preview': NVIDIA_API_KEY[:8] + '...' + NVIDIA_API_KEY[-4:] if len(NVIDIA_API_KEY) > 12 else '***'
+        })
+    return jsonify({'configured': False})
+
+# ============================================
+# ADMIN ROUTES
+# ============================================
+
+@app.route('/admin/login', methods=['GET', 'POST'])
+def admin_login():
+    if request.method == 'GET':
+        if session.get('admin_logged_in'):
+            return redirect(url_for('admin_dashboard'))
+        return render_template('login.html')
+
+    data = request.json
+    username = data.get('username', '').strip()
+    password = data.get('password', '').strip()
+
+    if not username or not password:
+        return jsonify({'success': False, 'error': 'Username and password required'}), 400
+
+    if not supabase:
+        return jsonify({'success': False, 'error': 'Database not available'}), 500
+
+    try:
+        result = supabase.table('admin_users').select('*').eq('username', username).execute()
+
+        if result.data and len(result.data) > 0:
+            user = result.data[0]
+
+            if user['password_hash'] == password:
+                session['admin_logged_in'] = True
+                session['admin_username'] = username
+                session['admin_id'] = user['id']
+
+                # Update last login
+                supabase.table('admin_users').update({
+                    'last_login': datetime.utcnow().isoformat()
+                }).eq('id', user['id']).execute()
+
+                return jsonify({'success': True, 'redirect': '/admin/dashboard'})
+
+        return jsonify({'success': False, 'error': 'Invalid credentials'}), 401
+
+    except Exception as e:
+        print(f"[ERROR] Login: {e}")
+        return jsonify({'success': False, 'error': str(e)}), 500
+
+@app.route('/admin/logout')
+def admin_logout():
+    session.pop('admin_logged_in', None)
+    session.pop('admin_username', None)
+    session.pop('admin_id', None)
+    return redirect(url_for('admin_login'))
+
+@app.route('/admin/dashboard')
+@login_required
+def admin_dashboard():
+    return render_template('admin.html', username=session.get('admin_username', 'Admin'))
+
+# ============================================
+# ANALYTICS API (OPTIMIZED)
+# ============================================
+
+@app.route('/api/analytics/stats')
+@login_required
+def get_analytics_stats():
+    if not supabase:
+        return jsonify({'error': 'Database not available'}), 500
+
+    try:
+        total_result = supabase.table('chat_messages').select('id, input_tokens, output_tokens, total_cost').execute()
+        total_questions = len(total_result.data) if total_result.data else 0
+
+        total_input_tokens = sum(msg.get('input_tokens', 0) or 0 for msg in (total_result.data or []))
+        total_output_tokens = sum(msg.get('output_tokens', 0) or 0 for msg in (total_result.data or []))
+        total_cost = sum(msg.get('total_cost', 0) or 0 for msg in (total_result.data or []))
+
+        sessions_result = supabase.table('chat_sessions').select('id').execute()
+        total_sessions = len(sessions_result.data) if sessions_result.data else 0
+
+        today = datetime.utcnow().date().isoformat()
+        today_result = supabase.table('chat_messages').select('id, input_tokens, output_tokens, total_cost').gte('created_at', today).execute()
+        today_questions = len(today_result.data) if today_result.data else 0
+        today_input_tokens = sum(msg.get('input_tokens', 0) or 0 for msg in (today_result.data or []))
+        today_output_tokens = sum(msg.get('output_tokens', 0) or 0 for msg in (today_result.data or []))
+        today_cost = sum(msg.get('total_cost', 0) or 0 for msg in (today_result.data or []))
+
+        avg_input_tokens = total_input_tokens / total_questions if total_questions > 0 else 0
+        avg_output_tokens = total_output_tokens / total_questions if total_questions > 0 else 0
+        avg_cost_per_message = total_cost / total_questions if total_questions > 0 else 0
+
+        return jsonify({
+            'total_questions': total_questions,
+            'total_sessions': total_sessions,
+            'today_questions': today_questions,
+            'total_input_tokens': total_input_tokens,
+            'total_output_tokens': total_output_tokens,
+            'total_tokens': total_input_tokens + total_output_tokens,
+            'total_cost': round(total_cost, 6),
+            'today_input_tokens': today_input_tokens,
+            'today_output_tokens': today_output_tokens,
+            'today_tokens': today_input_tokens + today_output_tokens,
+            'today_cost': round(today_cost, 6),
+            'avg_input_tokens': round(avg_input_tokens, 1),
+            'avg_output_tokens': round(avg_output_tokens, 1),
+            'avg_cost_per_message': round(avg_cost_per_message, 6),
+            'pricing': {
+                'input_per_1m': GROQ_COST_PER_1M_INPUT,
+                'output_per_1m': GROQ_COST_PER_1M_OUTPUT
+            },
+            'provider': 'groq'
+        })
+    except Exception as e:
+        print(f"[ERROR] Stats: {e}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/analytics/top-questions')
+@login_required
+def get_top_questions():
+    if not supabase:
+        return jsonify({'error': 'Database not available'}), 500
+
+    try:
+        limit = request.args.get('limit', 10, type=int)
+        result = supabase.table('chat_messages').select('user_question, created_at').execute()
+
+        if not result.data:
+            return jsonify({'questions': []})
+
+        question_counts = {}
+        for msg in result.data:
+            q = msg['user_question'].lower().strip()
+            if q in question_counts:
+                question_counts[q]['count'] += 1
+                if msg['created_at'] > question_counts[q]['last_asked']:
+                    question_counts[q]['last_asked'] = msg['created_at']
+                    question_counts[q]['original'] = msg['user_question']
+            else:
+                question_counts[q] = {
+                    'count': 1,
+                    'last_asked': msg['created_at'],
+                    'original': msg['user_question']
+                }
+
+        sorted_questions = sorted(
+            [{'question': v['original'], 'count': v['count'], 'last_asked': v['last_asked']}
+             for v in question_counts.values()],
+            key=lambda x: x['count'],
+            reverse=True
+        )[:limit]
+
+        return jsonify({'questions': sorted_questions})
+    except Exception as e:
+        print(f"[ERROR] Top questions: {e}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/analytics/all-questions')
+@login_required
+def get_all_questions():
+    if not supabase:
+        return jsonify({'error': 'Database not available'}), 500
+
+    try:
+        page = request.args.get('page', 1, type=int)
+        per_page = request.args.get('per_page', 20, type=int)
+        search = request.args.get('search', '').strip()
+
+        offset = (page - 1) * per_page
+
+        query = supabase.table('chat_messages').select('id, user_question, ai_response, created_at, response_time_ms')
+
+        if search:
+            query = query.ilike('user_question', f'%{search}%')
+
+        result = query.order('created_at', desc=True).range(offset, offset + per_page - 1).execute()
+
+        count_query = supabase.table('chat_messages').select('id', count='exact')
+        if search:
+            count_query = count_query.ilike('user_question', f'%{search}%')
+        count_result = count_query.execute()
+        total = count_result.count if count_result.count else 0
+
+        return jsonify({
+            'questions': result.data,
+            'total': total,
+            'page': page,
+            'per_page': per_page,
+            'total_pages': (total + per_page - 1) // per_page
+        })
+    except Exception as e:
+        print(f"[ERROR] All questions: {e}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/analytics/token-usage')
+@login_required
+def get_token_usage():
+    if not supabase:
+        return jsonify({'error': 'Database not available'}), 500
+
+    try:
+        result = supabase.table('chat_messages').select(
+            'created_at, input_tokens, output_tokens, total_cost'
+        ).order('created_at', desc=False).execute()
+
+        if not result.data:
+            return jsonify({'daily_usage': [], 'hourly_usage': []})
+
+        daily_data = {}
+        hourly_data = {}
+
+        for msg in result.data:
+            try:
+                created_at = datetime.fromisoformat(msg['created_at'].replace('Z', '+00:00'))
+                day_key = created_at.strftime('%Y-%m-%d')
+                hour_key = created_at.strftime('%Y-%m-%d %H:00')
+
+                input_tokens = msg.get('input_tokens', 0) or 0
+                output_tokens = msg.get('output_tokens', 0) or 0
+                cost = msg.get('total_cost', 0) or 0
+
+                if day_key not in daily_data:
+                    daily_data[day_key] = {
+                        'date': day_key,
+                        'input_tokens': 0,
+                        'output_tokens': 0,
+                        'total_tokens': 0,
+                        'cost': 0,
+                        'messages': 0
+                    }
+                daily_data[day_key]['input_tokens'] += input_tokens
+                daily_data[day_key]['output_tokens'] += output_tokens
+                daily_data[day_key]['total_tokens'] += input_tokens + output_tokens
+                daily_data[day_key]['cost'] += cost
+                daily_data[day_key]['messages'] += 1
+
+                if hour_key not in hourly_data:
+                    hourly_data[hour_key] = {
+                        'hour': hour_key,
+                        'input_tokens': 0,
+                        'output_tokens': 0,
+                        'total_tokens': 0,
+                        'cost': 0,
+                        'messages': 0
+                    }
+                hourly_data[hour_key]['input_tokens'] += input_tokens
+                hourly_data[hour_key]['output_tokens'] += output_tokens
+                hourly_data[hour_key]['total_tokens'] += input_tokens + output_tokens
+                hourly_data[hour_key]['cost'] += cost
+                hourly_data[hour_key]['messages'] += 1
+            except:
+                continue
+
+        daily_usage = sorted(daily_data.values(), key=lambda x: x['date'])[-30:]
+        hourly_usage = sorted(hourly_data.values(), key=lambda x: x['hour'])[-24:]
+
+        if daily_usage:
+            avg_daily_tokens = sum(d['total_tokens'] for d in daily_usage) / len(daily_usage)
+            avg_daily_cost = sum(d['cost'] for d in daily_usage) / len(daily_usage)
+            avg_daily_messages = sum(d['messages'] for d in daily_usage) / len(daily_usage)
+        else:
+            avg_daily_tokens = avg_daily_cost = avg_daily_messages = 0
+
+        return jsonify({
+            'daily_usage': daily_usage,
+            'hourly_usage': hourly_usage,
+            'projections': {
+                'avg_daily_tokens': round(avg_daily_tokens, 0),
+                'avg_daily_cost': round(avg_daily_cost, 4),
+                'avg_daily_messages': round(avg_daily_messages, 1),
+                'projected_monthly_tokens': round(avg_daily_tokens * 30, 0),
+                'projected_monthly_cost': round(avg_daily_cost * 30, 2),
+                'projected_monthly_messages': round(avg_daily_messages * 30, 0)
+            }
+        })
+    except Exception as e:
+        print(f"[ERROR] Token usage: {e}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/admin/check')
+def check_admin():
+    return jsonify({
+        'is_admin': session.get('admin_logged_in', False),
+        'username': session.get('admin_username', None)
+    })
+
+# ============================================
+# MAIN
+# ============================================
+
+if __name__ == '__main__':
+    print("\n" + "="*60)
+    print("RNGPIT AI ASSISTANT - NVIDIA API (Llama 3.1) VERSION")
+    print("="*60)
+    print(f"Data: {TEXT_FILE_PATH}")
+    print(f"Available Models: {list(NVIDIA_MODEL_REGISTRY.keys())}")
+    print(f"Default Model: {DEFAULT_NVIDIA_MODEL}")
+    print(f"Supabase: {'✓' if supabase else '✗'}")
+    print(f"NVIDIA API Key: {'✓ Configured' if NVIDIA_API_KEY else '✗ Not set (use NVIDIA_API_KEY env var)'}")
+    print("="*60)
+
+    initialize_rag()
+
+    print("\n🚀 Server starting...")
+    print("📱 Chatbot: http://localhost:5000")
+    print("🔐 Admin: http://localhost:5000/admin/login")
+    print("\n⚡ NVIDIA API Features:")
+    print("  ✓ Dracarys Llama 3.1 70B via NVIDIA")
+    print("  ✓ Available models:")
+    for model_id, config in NVIDIA_MODEL_REGISTRY.items():
+        print(f"    - {model_id}: {config['description']}")
+    print("  ✓ OpenAI-compatible API")
+    print("  ✓ Powerful 70B parameter model")
+    print("  ✓ Same RAG pipeline as original")
+    print("  ✓ Same admin panel and analytics")
+    print("  ✓ No local GPU required!")
+    print("="*60 + "\n")
+
+    app.run(debug=False, port=5000, host='0.0.0.0', threaded=True)

docker-compose.yml

@@ -0,0 +1,16 @@
+version: '3.8'
+
+services:
+  chatbot:
+    build: .
+    ports:
+      - "5000:5000"
+    env_file:
+      - .env
+    restart: always
+    volumes:
+      - ./data:/app/data
+      - model_cache:/app/model_cache
+
+volumes:
+  model_cache:

requirements.txt

@@ -1,11 +1,11 @@
-flask==3.0.0
-flask-cors==4.0.0
-sentence-transformers==2.7.0
-torch==2.1.2
-transformers==4.40.0
-chromadb==0.4.22
-groq==0.4.2
-supabase==2.3.4
-numpy==1.24.3
-requests==2.31.0
-gunicorn==21.2.0
+flask==3.0.0
+flask-cors==4.0.0
+sentence-transformers==2.7.0
+torch==2.1.2
+transformers==4.40.0
+chromadb==0.4.22
+openai>=1.0.0
+supabase==2.3.4
+numpy==1.24.3
+requests==2.31.0
+python-dotenv==1.0.0

static/AA240126014782K_RC03012026.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9eb54a528411216cb921030aace977c21618eb735ad64bfc46cc9906a44b788a
+size 225869

static/CERTIFICATE OF RECOGNITION.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d815225f96d39f540713bb1c8d0a12bed2c32650fbf79c4666ac62ceb647636
+size 1071880

static/new Print _ Udyam Registration Certificate.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa6965efe589669ec6a8f3e7e5b37565c8bc894460728b738e21cabfc1b8b882
+size 260688

templates/index.html

@@ -5,6 +5,7 @@
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <title>RNG Patel Institute AI Assistant</title>
+    <link rel="icon" type="image/png" href="{{ url_for('static', filename='RNGPIT.png') }}">
     <link href="https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700;800&display=swap"
         rel="stylesheet">
     <style>
@@ -101,18 +102,18 @@
         .logo {
             width: 48px;
             height: 48px;
-            background: linear-gradient(135deg, var(--accent-primary), var(--accent-secondary));
             border-radius: 12px;
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            font-weight: 800;
-            font-size: 18px;
-            color: white;
+            overflow: hidden;
             box-shadow: 0 8px 32px var(--shadow-color);
             transition: transform 0.3s ease;
         }
 
+        .logo img {
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+        }
+
         .logo:hover {
             transform: scale(1.05) rotate(5deg);
         }
@@ -202,19 +203,19 @@
         .welcome-logo {
             width: 120px;
             height: 120px;
-            background: linear-gradient(135deg, var(--accent-primary), var(--accent-secondary));
             border-radius: 30px;
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            font-weight: 800;
-            font-size: 48px;
-            color: white;
+            overflow: hidden;
             margin-bottom: 32px;
             box-shadow: 0 20px 60px var(--shadow-color);
             animation: float 3s ease-in-out infinite;
         }
 
+        .welcome-logo img {
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+        }
+
         @keyframes float {
 
             0%,
@@ -873,6 +874,53 @@
             .header-title {
                 font-size: 14px;
             }
+
+            /* Optimized Mobile Layout */
+            .message-group {
+                display: grid;
+                grid-template-columns: auto 1fr;
+                column-gap: 12px;
+                row-gap: 4px;
+                align-items: center;
+            }
+
+            .avatar {
+                width: 32px;
+                height: 32px;
+                font-size: 16px;
+                grid-column: 1;
+                grid-row: 1;
+            }
+
+            .message-content {
+                display: contents;
+                /* Unwrap so children become grid items */
+            }
+
+            .message-header {
+                grid-column: 2;
+                grid-row: 1;
+                margin-bottom: 0;
+                font-size: 13px;
+                align-self: center;
+                /* Align with avatar center */
+            }
+
+            .message-text {
+                grid-column: 1 / -1;
+                /* Span full width (row 2) */
+                grid-row: 2;
+                max-width: 100%;
+                margin-top: 4px;
+                /* Small gap from header */
+                width: 100%;
+                /* Force width */
+            }
+
+            .debug-panel {
+                grid-column: 1 / -1;
+                grid-row: 3;
+            }
         }
     </style>
 </head>
@@ -883,26 +931,24 @@
     <div class="app-container">
         <div class="header">
             <div class="header-left">
-                <div class="logo">RNG</div>
-                <div class="header-title">RNG Patel Institute AI</div>
+                <div class="logo">
+                    <img src="{{ url_for('static', filename='RNGPIT.png') }}" alt="RNGPIT Logo">
+                </div>
+                <div class="header-title">RNG Patel Institute of Technology AI</div>
             </div>
             <div class="header-right">
                 <button class="icon-btn" onclick="toggleTheme()" title="Toggle Theme">
                     <span id="themeIcon">🌙</span>
                 </button>
-                <a href="/admin/login" class="icon-btn" title="Admin Dashboard" style="text-decoration: none;">
-                    📊
-                </a>
-                <button class="icon-btn" onclick="clearChat()" title="New Chat">
-                    🔄
-                </button>
             </div>
         </div>
 
         <div class="chat-container" id="chatContainer">
             <div class="welcome-screen" id="welcomeScreen">
-                <div class="welcome-logo">RNG</div>
-                <div class="welcome-title">Welcome to RNG Patel Institute</div>
+                <div class="welcome-logo">
+                    <img src="{{ url_for('static', filename='RNGPIT.png') }}" alt="RNGPIT Logo">
+                </div>
+                <div class="welcome-title">Welcome to RNG Patel Institute of Technology</div>
                 <div class="welcome-subtitle">Your AI-powered assistant for everything about courses, admissions,
                     facilities, placements, and campus life</div>
                 <div class="quick-prompts">
@@ -1066,7 +1112,7 @@
 
                 const messageHeader = document.createElement('div');
                 messageHeader.className = 'message-header';
-                messageHeader.textContent = isUser ? 'You' : 'RNG Patel AI';
+                messageHeader.textContent = isUser ? 'You' : 'RNGPIT AI';
 
                 const messageText = document.createElement('div');
                 messageText.className = 'message-text';
@@ -1172,7 +1218,7 @@
 
                 const messageHeader = document.createElement('div');
                 messageHeader.className = 'message-header';
-                messageHeader.textContent = 'RNG Patel AI';
+                messageHeader.textContent = 'RNGPIT AI';
 
                 const messageText = document.createElement('div');
                 messageText.className = 'message-text';