Update app.py

app.py

@@ -1,30 +1,123 @@
 # app.py
-from flask import Flask, request, jsonify
+from flask import Flask, request, jsonify, render_template
 import requests
 import json
-import asyncio # Import asyncio for running async functions
+import asyncio
+import os # Import os to access environment variables
 
 app = Flask(__name__)
 
-# (Include your generate_solution_python function here,
-#  making sure to adjust the API endpoint for google_search if necessary)
+# --- Your AI Tool Logic (Modified for Hugging Face) ---
+async def generate_solution_python(user_query):
+    """
+    Generates a solution using a dummy context (since google_search is not available)
+    and Gemini LLM.
 
-# Assuming generate_solution_python is defined as in your Canvas
-# You'll need to make sure the requests.post for google_search points to a valid endpoint
-# In a real Hugging Face Space, you might not have a direct /api/google_search.
-# You'd need to consider how to integrate a search API (e.g., Google Custom Search API, SerpAPI)
-# and handle its API key securely.
+    Args:
+        user_query (str): The query provided by the user.
+    """
+    if not user_query:
+        return "Please enter your query to get a solution."
+
+    print(f"Processing query: {user_query}")
+    response_text = ""
+
+    try:
+        # --- IMPORTANT: Placeholder for Search API Integration ---
+        # The 'google_search' tool is specific to the Canvas environment.
+        # On Hugging Face, you would integrate a real public search API here,
+        # e.g., Google Custom Search API, SerpAPI, or a web scraping library.
+        # For this example, we'll use a dummy context.
+        dummy_context = f"Information related to '{user_query}' from various online sources indicates that..."
+        
+        # In a real scenario, you'd make an API call like this (example with a hypothetical search API):
+        # search_api_key = os.environ.get("YOUR_SEARCH_API_KEY")
+        # search_api_url = "https://api.example.com/search"
+        # search_response = requests.get(search_api_url, params={"q": user_query, "api_key": search_api_key})
+        # search_response.raise_for_status()
+        # search_results = search_response.json()
+        # context = process_search_results(search_results) # A function to extract snippets
+
+        context = dummy_context # Using dummy context for now
+
+        # Step 2: Construct prompt for LLM with context
+        chat_history = []
+        prompt = f"""You are an AI assistant that provides comprehensive solutions based on the given query and additional context from open sources.
+
+User Query: {user_query}
+
+Relevant Open-Source Information:
+{context}
+
+Please provide a detailed and helpful solution, incorporating the provided information where relevant. If the information is insufficient, state that and provide a general answer.
+"""
+
+        chat_history.append({"role": "user", "parts": [{"text": prompt}]})
+
+        # Step 3: Call Gemini API
+        print("Calling Gemini API...")
+        llm_payload = {
+            "contents": chat_history
+        }
+
+        # Get API key from environment variables (Hugging Face Space Secrets)
+        gemini_api_key = os.environ.get("GEMINI_API_KEY") 
+        if not gemini_api_key:
+            raise ValueError("GEMINI_API_KEY environment variable not set.")
+
+        gemini_api_url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={gemini_api_key}"
+
+        gemini_response = requests.post(
+            gemini_api_url,
+            headers={'Content-Type': 'application/json'},
+            data=json.dumps(llm_payload)
+        )
+
+        gemini_response.raise_for_status() # Raise an exception for HTTP errors
+        llm_result = gemini_response.json()
+        print("Gemini API response received.")
+
+        if llm_result.get('candidates') and len(llm_result['candidates']) > 0 and \
+           llm_result['candidates'][0].get('content') and llm_result['candidates'][0]['content'].get('parts') and \
+           len(llm_result['candidates'][0]['content']['parts']) > 0:
+            response_text = llm_result['candidates'][0]['content']['parts'][0]['text']
+        else:
+            response_text = "No solution could be generated. Please try a different query."
+
+    except requests.exceptions.RequestException as e:
+        error_message = f"Network or API error: {e}"
+        print(f"Error: {error_message}")
+        response_text = f"An error occurred: {error_message}. Please check the logs for details."
+    except ValueError as e:
+        error_message = f"Configuration error: {e}"
+        print(f"Error: {error_message}")
+        response_text = f"An error occurred: {error_message}. Please check the logs for details."
+    except Exception as e:
+        error_message = f"An unexpected error occurred: {e}"
+        print(f"Error: {error_message}")
+        response_text = f"An error occurred: {error_message}. Please check the logs for details."
+    
+    return response_text
+
+# --- Flask Routes ---
+
+@app.route('/')
+def index():
+    """Serves the main HTML page."""
+    return render_template('index.html')
 
 @app.route('/generate', methods=['POST'])
 async def generate():
+    """Handles the AI generation request."""
     data = request.json
     user_query = data.get('query')
     if not user_query:
         return jsonify({"error": "Query is required"}), 400
 
     # Run the async function
-    response_text = await generate_solution_python(user_query)
-    return jsonify({"solution": response_text})
+    solution = await generate_solution_python(user_query)
+    return jsonify({"solution": solution})
 
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860) # Hugging Face Spaces typically use port 7860
+    # Hugging Face Spaces typically expect the app to run on port 7860
+    app.run(host='0.0.0.0', port=7860)