Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,79 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from huggingface_hub import hf_hub_download
+import os
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# --- CONFIGURATION ---
+MODEL_REPO = "bharatgenai/Param2-17B-A2.4B-Thinking"
+# Note: Replace this with the actual GGUF repo once it is confirmed live
+GGUF_REPO = "DarkWolfX/Param2-17B-A2.4B-Thinking-GGUF" 
+GGUF_FILE = "param2-thinking-q4_k_m.gguf"
+
+# --- UI SETUP ---
+st.set_page_config(page_title="Param2 Multilingual Chat", layout="centered")
+st.title("🇮🇳 Param2-17B Thinking Chatbot")
+
+# Language Selection Dropdown
+languages = [
+    "English", "Hindi", "Assamese", "Bengali", "Bodo", "Dogri", "Gujarati", 
+    "Kannada", "Konkani", "Kashmiri", "Maithili", "Malayalam", "Manipuri", 
+    "Marathi", "Nepali", "Oriya", "Punjabi", "Sanskrit", "Santali", 
+    "Sindhi", "Tamil", "Telugu", "Urdu"
+]
+selected_lang = st.selectbox("Select Response Language:", languages)
+
+# --- MODEL LOADING LOGIC ---
+@st.cache_resource
+def load_model():
+    try:
+        # 1. Check if GGUF exists and download
+        model_path = hf_hub_download(repo_id=GGUF_REPO, filename=GGUF_FILE)
+        
+        # 2. Initialize llama-cpp (Optimized for CPU)
+        from llama_cpp import Llama
+        llm = Llama(
+            model_path=model_path,
+            n_ctx=4096,
+            n_threads=8, # Optimized for your 8 vCPU Space
+        )
+        return llm
+    except Exception as e:
+        return f"Error: GGUF model not found or incompatible. {str(e)}"
+
+# Attempt to load
+llm = load_model()
+
+if isinstance(llm, str):
+    st.error(llm)
+    st.info("The GGUF version of this model might not be available yet. Please check back later!")
+else:
+    # --- CHAT INTERFACE ---
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    if prompt := st.chat_input("Ask something..."):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        with st.chat_message("assistant"):
+            # System Prompt Injection for Language
+            system_instruction = f"You are a helpful assistant. You must respond ONLY in {selected_lang}."
+            full_prompt = f"<|system|>\n{system_instruction}\n<|user|>\n{prompt}\n<|assistant|>\n"
+            
+            # Generate response
+            response_container = st.empty()
+            full_response = ""
+            
+            # Stream the response for a better UI feel
+            for chunk in llm(full_prompt, max_tokens=1024, stream=True):
+                text = chunk["choices"][0]["text"]
+                full_response += text
+                response_container.markdown(full_response + "▌")
+            
+            response_container.markdown(full_response)
+        
+        st.session_state.messages.append({"role": "assistant", "content": full_response})