update

Procfile

@@ -0,0 +1 @@
+web: uvicorn api_server:app --host 0.0.0.0 --port 7860

README.md

@@ -1,11 +1,9 @@
 ---
 title: Aura Mind Glow
-emoji: 📉
+emoji: 🌻
 colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 5.42.0
-app_file: app.py
+colorTo: blue
+sdk: docker
 pinned: false
 ---
 

api_server.py

@@ -0,0 +1,131 @@
+# ==============================================================================
+# Aura Mind Glow - Main Server (FastAPI + Gradio)
+# ==============================================================================
+"""
+This script is the main entry point for the application. It launches a FastAPI
+server that provides the diagnosis API and also serves the entire Gradio UI.
+
+To run this server for development:
+1. Make sure you have installed all packages from requirements.txt.
+2. Run the command: uvicorn api_server:app --host 127.0.0.1 --port 7860
+
+When deployed to Hugging Face Spaces, the Procfile will handle this command.
+"""
+
+# --- Essential Imports ---
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse
+from PIL import Image
+import os
+import warnings
+import tempfile
+import re
+import io
+import gradio as gr
+
+# --- Import Core Components from Modules ---
+# This setup is now shared between the API and the Gradio App
+from vision_model import load_vision_model
+from knowledge_base import KnowledgeBase
+from agent_setup import initialize_adk
+from bigquery_search import search_bigquery_for_remedy
+from vector_store import embed_and_store_documents
+
+# --- Import the Gradio UI from app.py ---
+# We import the 'demo' object directly. The app.py script should not call demo.launch()
+try:
+    from app import demo as gradio_app
+    print("✅ Gradio UI imported successfully from app.py.")
+except ImportError as e:
+    gradio_app = None
+    print(f"❌ CRITICAL: Could not import Gradio UI from app.py: {e}")
+    print("Ensure app.py defines a Gradio Blocks object named 'demo' and does not call .launch().")
+
+
+print("✅ All server libraries imported successfully.")
+
+# --- Global Initialization ---
+warnings.filterwarnings("ignore")
+os.environ["TORCH_COMPILE_DISABLE"] = "1"
+
+print("Performing initial setup for server (this may take a moment)...")
+VISION_MODEL, PROCESSOR = load_vision_model()
+KB = KnowledgeBase()
+RETRIEVER = KB
+embed_and_store_documents()
+
+adk_components = initialize_adk(VISION_MODEL, PROCESSOR, RETRIEVER)
+DIAGNOSIS_TOOL = adk_components["diagnosis_tool"] if adk_components else None
+
+if not DIAGNOSIS_TOOL:
+    print("❌ CRITICAL: Diagnosis tool could not be initialized. The API will not work.")
+
+print("✅ Server setup complete.")
+
+# --- FastAPI App and Endpoint Logic ---
+app = FastAPI(
+    title="Aura Mind Glow API",
+    description="Provides access to the plant diagnosis model and serves the Gradio UI.",
+    version="1.0.0",
+)
+
+def run_diagnosis_logic(image: Image.Image):
+    """
+    Core logic for running diagnosis and getting remedies.
+    """
+    temp_file_path = None
+    try:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
+            image.save(temp_file.name)
+            temp_file_path = temp_file.name
+
+        diagnosis = DIAGNOSIS_TOOL(temp_file_path)
+        if "Could not parse" in diagnosis:
+            return {"error": f"Could not identify condition: {diagnosis}"}
+
+        cleaned_diagnosis = re.sub(r'[^\w\s.\\-,\"]', '', diagnosis)
+        cleaned_diagnosis = re.sub(r'\s+', ' ', cleaned_diagnosis).strip()
+
+        local_remedy_list = search_documents(cleaned_diagnosis)
+        local_remedy = local_remedy_list[0] if local_remedy_list else "No remedy found in local knowledge base."
+
+        search_query = "healthy maize" if "healthy" in cleaned_diagnosis.lower() else "phosphorus" if "phosphorus" in cleaned_diagnosis.lower() else "general"
+        cloud_remedy = search_bigquery_for_remedy(search_query)
+
+        return {
+            "diagnosis": diagnosis,
+            "remedy_local": local_remedy,
+            "remedy_cloud": cloud_remedy
+        }
+    finally:
+        if temp_file_path:
+            os.remove(temp_file_path)
+
+@app.post("/diagnose/", tags=["Diagnosis"])
+async def diagnose_endpoint(file: UploadFile = File(...)):
+    """
+    Receives an image file, performs diagnosis, and returns the result as JSON.
+    """
+    if not file.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="File provided is not an image.")
+
+    try:
+        image_bytes = await file.read()
+        image = Image.open(io.BytesIO(image_bytes))
+        result = run_diagnosis_logic(image)
+
+        if "error" in result:
+            raise HTTPException(status_code=500, detail=result["error"])
+
+        return JSONResponse(content=result)
+    except Exception as e:
+        print(f"❌ API Error: {e}")
+        raise HTTPException(status_code=500, detail=f"An internal server error occurred: {e}")
+
+# --- Mount the Gradio App ---
+if gradio_app:
+    app = gr.mount_gradio_app(app, gradio_app, path="/")
+    print("✅ Gradio UI has been mounted on the FastAPI server at the root path '/'.")
+
+# Note: The 'if __name__ == "__main__":' block with uvicorn.run() is removed.
+# The Procfile will be used by Hugging Face to run the server.

app.py

@@ -134,7 +134,7 @@ def create_field_mode_ui(user_state):
     """Creates the Gradio UI for the offline Field Mode."""
 
     def clean_diagnosis_text(diagnosis: str) -> str:
-        cleaned_text = re.sub(r'[^\w\s.\-,\"]', '', diagnosis)
+        cleaned_text = re.sub(r'[^\w\s.\-,"]', '', diagnosis)
         cleaned_text = re.sub(r'\s+', ' ', cleaned_text).strip()
         return cleaned_text
 
@@ -577,4 +577,4 @@ if __name__ == "__main__":
             outputs=[user_state, login_view, main_view, logged_in_user_display]
         )
 
-    demo.launch(share=True, debug=True)
+    # demo.launch() is removed because the app is now launched by api_server.py

requirements.txt

@@ -20,4 +20,7 @@ google-genai
 google-adk
 google-cloud-bigquery
 requests
-faiss-cpu
+faiss-cpu
+fastapi
+python-multipart
+uvicorn