Update app.py

app.py

@@ -1,10 +1,11 @@
-# Step 1: Import libraries (Ensure these are in requirements.txt)
+# Step 1: Import libraries
 import gradio as gr
 import google.generativeai as genai
 from duckduckgo_search import DDGS
 import os
 import textwrap
-import traceback # For detailed error logging if needed
+import traceback # For detailed error logging
+import time      # For retry delay
 
 # --- Step 2: Configure API Key (Using Hugging Face Secrets) ---
 is_api_configured = False
@@ -12,41 +13,38 @@ GOOGLE_API_KEY = None
 
 print("⚙️ Attempting to configure Google API Key from HF Space secret...")
 try:
-    # Read the secret value set in the Hugging Face Space settings
     GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
-
     if GOOGLE_API_KEY:
         genai.configure(api_key=GOOGLE_API_KEY)
         print("✅ Google API Key configured successfully from HF secret.")
-        is_api_configured = True # Set flag to True ONLY if configure() succeeds
+        is_api_configured = True
     else:
-        # Secret variable not found or empty in HF Space settings
         print("❌ Error: GOOGLE_API_KEY secret not found or is empty in Space settings.")
-        print("➡️ Please go to your Space Settings -> Secrets and ensure 'GOOGLE_API_KEY' is added with your API key value.")
+        print("➡️ Please go to your Space Settings -> Secrets and ensure 'GOOGLE_API_KEY' is added.")
         is_api_configured = False
-
 except Exception as e:
     print(f"❌ An unexpected error occurred during API Key configuration: {e}")
-    is_api_configured = False # Not configured if any other error occurs
-    traceback.print_exc() # Print detailed traceback in HF logs for debugging
+    is_api_configured = False
+    traceback.print_exc()
 
 # --- End of API Key Configuration ---
 
 
 # Step 3: Define Helper Functions
 
-# Function to perform web search
-def search_web(query, num_results=7):
+# Function to perform web search (with increased timeout)
+def search_web(query, num_results=7, search_timeout=20): # Added timeout parameter
     """Searches the web using DuckDuckGo and returns formatted results."""
-    print(f"🔍 Searching the web for: '{query}'...")
+    print(f"🔍 Searching the web for: '{query}' (Timeout: {search_timeout}s)...")
     try:
-        with DDGS() as ddgs:
+        # Increase the timeout when initializing DDGS
+        with DDGS(timeout=search_timeout) as ddgs:
             results = list(ddgs.text(query, region='wt-wt', safesearch='off', max_results=num_results))
             if not results:
                 print("⚠️ No search results found.")
                 return "No relevant search results found for the query."
 
-            # Format results for the LLM
+            # Format results
             context = f"Search results for query '{query}':\n\n"
             for i, result in enumerate(results):
                 context += f"Source [{i+1}]: {result.get('title', 'N/A')}\n"
@@ -59,49 +57,40 @@ def search_web(query, num_results=7):
     except Exception as e:
         print(f"❌ Error during web search: {e}")
         traceback.print_exc() # Log details in HF
-        return f"Error occurred during web search. Details: {e}"
+        # Make error message more specific for timeouts
+        error_detail = f"Details: {e}"
+        if "timed out" in str(e):
+            error_detail = f"Details: The connection to the search engine timed out after {search_timeout} seconds. This might be due to temporary network issues. Error: {e}"
+        return f"Error occurred during web search. {error_detail}"
 
 # Function to generate the case study using Gemini
 def generate_case_study(topic, search_context):
     """Generates a case study using Gemini based on the topic and search context."""
     print(f"🤖 Generating case study for: '{topic}'...")
 
-    # --- Check 1: API Configuration ---
+    # Check 1: API Configuration
     if not is_api_configured:
-         print("❌ Cannot generate: Google API Key not configured successfully.")
-         # Provide error message tailored for HF environment
-         return "Error: Google API Key not configured successfully. Please check the GOOGLE_API_KEY secret in your Hugging Face Space settings and ensure it's correct. The space might need a restart after setting the secret."
+         print("❌ Cannot generate: Google API Key not configured.")
+         return "Error: Google API Key not configured successfully. Check HF Space secrets."
 
-    # --- Check 2: Search Results ---
+    # Check 2: Search Results Validity
     if "Error occurred during web search" in search_context or "No relevant search results found" in search_context:
          print(f"❌ Cannot generate: Problem with search results.")
          return f"Cannot generate case study due to search issues:\n{search_context}"
 
-    # --- Configure the Gemini model ---
+    # Configure the Gemini model
     model_name = 'gemini-1.5-flash-latest'
     try:
         print(f"   Using model: {model_name}")
         model = genai.GenerativeModel(model_name)
     except Exception as e:
         print(f"❌ Error initializing GenerativeModel '{model_name}': {e}")
-        traceback.print_exc() # Log details in HF
-        # Try to list available models if initialization fails
+        traceback.print_exc()
         error_message = f"Error setting up the AI model '{model_name}': {e}."
-        try:
-            available_models = [m.name for m in genai.list_models() if 'generateContent' in m.supported_generation_methods]
-            print(f"   Available models supporting generateContent: {available_models}")
-            if available_models:
-                 suggested_model = next((m for m in available_models if 'flash' in m or 'pro' in m), available_models[0])
-                 error_message += f" You could try updating the model name in app.py to one of these like: '{suggested_model.split('/')[-1]}'"
-            else:
-                 error_message += " Additionally, no compatible models were found via ListModels."
-        except Exception as list_e:
-             print(f"   Additionally failed to list available models: {list_e}")
-             error_message += " Failed to list alternative models."
+        # (Optional: Add model listing code back here if needed for debugging)
         return error_message
 
-
-    # --- Define the Prompt ---
+    # Define the Prompt (Keep your detailed prompt here)
     prompt = f"""
     You are an expert business analyst and case study writer.
     Your task is to generate a comprehensive case study based on the following topic: "{topic}"
@@ -110,22 +99,22 @@ def generate_case_study(topic, search_context):
 
     **Required Case Study Format:**
 
-    **1. Title:** Create a concise and informative title based on the topic and findings.
-    **2. Introduction/Executive Summary:** Briefly introduce the subject and the core topic. State the key outcome *mentioned in the sources*.
-    **3. The Company/Subject:** Provide background information *only from the search results*.
-    **4. The Challenge/Problem:** Describe the specific business issue mentioned in the sources.
-    **5. The Solution:** Detail the implemented solution *based only on the sources*.
-    **6. Implementation/Process:** (Optional) Describe *only if information is available in the sources*.
-    **7. Results/Impact:** Quantify results and impact using data *from the sources*. If no results are mentioned, state that.
-    **8. Conclusion:** Summarize key takeaways *based on the provided information*.
-    **9. Sources:** List the URLs (`URL:` lines) from the search results that were most relevant.
+    **1. Title:** Create a concise and informative title.
+    **2. Introduction/Executive Summary:** Briefly introduce the subject and core topic. State key outcome from sources.
+    **3. The Company/Subject:** Background info from search results only.
+    **4. The Challenge/Problem:** Specific issue mentioned in sources.
+    **5. The Solution:** Implemented solution based only on sources.
+    **6. Implementation/Process:** (Optional) Describe only if available in sources.
+    **7. Results/Impact:** Quantify results using data from sources. State if none mentioned.
+    **8. Conclusion:** Summarize key takeaways based on provided info.
+    **9. Sources:** List relevant URLs from search results.
 
     **Instructions:**
-    *   Adhere strictly to the format above. Use Markdown `##` for section headings.
-    *   Base your writing ***exclusively*** on the information in the "Provided Search Context". Do not invent information.
-    *   If details for a section are missing in the sources, explicitly state: "Information not available in the provided sources."
-    *   Maintain an objective and professional tone.
-    *   Format the output using Markdown.
+    *   Adhere strictly to the format (use Markdown `##`).
+    *   Base writing ***exclusively*** on "Provided Search Context". Do not invent.
+    *   If details missing, state: "Information not available in the provided sources."
+    *   Maintain objective tone.
+    *   Format using Markdown.
 
     **Provided Search Context:**
     ---
@@ -135,103 +124,113 @@ def generate_case_study(topic, search_context):
     Now, please generate the case study for "{topic}".
     """
 
-    # --- Generate Content ---
+    # Generate Content
     try:
         response = model.generate_content(prompt)
-
-        # --- Process Response Safely ---
+        # Process Response Safely (Keep the detailed checking from previous version)
         if response.parts:
             generated_text = "".join(part.text for part in response.parts)
             print("✅ Case study generated successfully.")
             return generated_text
         elif response.prompt_feedback and response.prompt_feedback.block_reason:
              block_reason = response.prompt_feedback.block_reason
-             safety_info = f" Ratings: {response.prompt_feedback.safety_ratings}" if response.prompt_feedback.safety_ratings else ""
              print(f"⚠️ Generation blocked due to: {block_reason}")
-             return f"Error: Generation failed. Blocked due to '{block_reason}'.{safety_info} Please try refining your topic or check content policies."
+             return f"Error: Generation failed. Blocked due to '{block_reason}'. Check content policies."
         elif not response.candidates:
              finish_reason = response.candidates[0].finish_reason if response.candidates else "UNKNOWN"
-             print(f"⚠️ Warning: Generation finished without valid content (Finish Reason: {finish_reason}).")
-             return f"Error: The AI model finished generation but produced no usable content (Reason: {finish_reason}). Check model compatibility or prompt complexity."
+             print(f"⚠️ Generation finished without valid content (Reason: {finish_reason}).")
+             return f"Error: AI model finished but produced no usable content (Reason: {finish_reason})."
         else:
-             print("⚠️ Warning: Generation finished but produced no text content for unknown reasons.")
-             return "Error: The AI model generated an empty response. This might be due to input, filters, or a temporary issue."
+             print("⚠️ Generation produced no text content.")
+             return "Error: AI model generated an empty response."
 
     except Exception as e:
         print(f"❌ Error during case study generation: {e}")
-        traceback.print_exc() # Log details in HF
+        traceback.print_exc()
         error_message = f"An unexpected error occurred during AI generation: {e}"
-        # Add specific error checks relevant to Gemini API
-        if "API key not valid" in str(e) or "PermissionDenied" in str(e) or "AuthenticationError" in str(e):
-             error_message = "Error: Invalid, expired, or missing API Key. Please double-check the GOOGLE_API_KEY secret in Space settings and ensure the Gemini API is enabled in your Google Cloud project."
+        # Add specific error checks (keep from previous version)
+        if "API key not valid" in str(e) or "PermissionDenied" in str(e):
+             error_message = "Error: Invalid/Missing API Key. Check GOOGLE_API_KEY secret and Gemini API enablement."
         elif "Model not found" in str(e):
-             error_message = f"Error: The AI model ('{model_name}') was not found or is unsupported. Check the model name in app.py or try updating the google-generativeai library in requirements.txt."
+             error_message = f"Error: AI model ('{model_name}') not found/unsupported."
         elif "Resource has been exhausted" in str(e) or "Quota" in str(e):
-             error_message = "Error: API quota exceeded. Check your usage limits in Google Cloud Console."
-        elif hasattr(e, 'message') and str(e) != e.message:
-             error_message = f"Error during AI generation: {e.message} (Details: {e})"
+             error_message = "Error: API quota exceeded. Check Google Cloud Console."
         return error_message
 
 
-# Step 4: Define the main processing function for Gradio
+# Step 4: Define the main processing function (with search retries)
 def create_case_study(company_or_topic):
-    """Orchestrates the web search and case study generation process."""
-    print("-" * 60) # Separator for new request
+    """Orchestrates the web search (with retries) and case study generation."""
+    print("-" * 60)
     if not company_or_topic or not company_or_topic.strip():
-        print("⚠️ Input validation failed: Empty topic received.")
+        print("⚠️ Input validation failed: Empty topic.")
         return "Please enter a valid company name or topic."
 
     cleaned_topic = company_or_topic.strip()
     print(f"➡️ Processing request for: '{cleaned_topic}'")
 
-    # 1. Search the web
-    search_results_context = search_web(cleaned_topic)
-
-    # 2. Generate the case study (handles API key/search errors internally)
+    # --- Search with Retries ---
+    search_results_context = None
+    max_retries = 2  # Total attempts = 1 (initial) + max_retries
+    retry_delay_seconds = 3
+    search_timeout_seconds = 25 # You can adjust this timeout specifically for search
+
+    for attempt in range(max_retries + 1):
+        print(f"   Attempting web search ({attempt + 1}/{max_retries + 1})...")
+        search_results_context = search_web(cleaned_topic, search_timeout=search_timeout_seconds)
+
+        # Check if search was successful
+        if search_results_context and "Error occurred during web search" not in search_results_context:
+            print("   Web search successful.")
+            break # Exit loop on success
+
+        # If search failed and retries remain
+        if attempt < max_retries:
+            print(f"   Search attempt failed. Waiting {retry_delay_seconds}s before retrying...")
+            time.sleep(retry_delay_seconds)
+        else:
+            # Max retries reached
+            print(f"   Search failed after {max_retries + 1} attempts.")
+            # Return the error from the last attempt directly
+            print("-" * 60)
+            return f"Failed to retrieve search results after multiple attempts.\nLast error: {search_results_context}"
+
+    # --- Generate Case Study (only if search succeeded) ---
+    # This part is reached only if the loop above 'break's (i.e., search succeeded)
     case_study_markdown = generate_case_study(cleaned_topic, search_results_context)
 
-    print("-" * 60) # Separator for end of request
+    print("-" * 60)
     return case_study_markdown
 
 # Step 5: Create and Launch the Gradio Interface
 print("\n⚙️ Setting up Gradio interface...")
 
-# Add a final check before defining Gradio Interface (optional but good practice)
 if not is_api_configured:
-    print("\n" + "="*60)
-    print("‼️ WARNING: Google API Key not configured successfully at startup. ‼️")
-    print("   The Gradio interface will launch, but case study generation WILL FAIL until the API key is correctly set in secrets and the space potentially restarted.")
-    print("="*60 + "\n")
+    print("\n" + "="*60 + "\n‼️ WARNING: API Key not configured at startup. Generation will fail. Check Secrets.\n" + "="*60 + "\n")
 
-# Define the Gradio interface
 iface = gr.Interface(
     fn=create_case_study,
     inputs=gr.Textbox(
         lines=2,
-        placeholder="Enter a company name or topic (e.g., 'Acme Corp uses AI for customer support' or 'History of Netflix recommendation engine')",
+        placeholder="Enter a company name or topic (e.g., 'Acme Corp uses AI for customer support')",
         label="Company Name or Topic"
     ),
-    outputs=gr.Markdown( # Use Markdown output for better formatting
-        label="Generated Case Study"
-    ),
+    outputs=gr.Markdown(label="Generated Case Study"),
     title="📄 AI Case Study Generator (Gemini + DuckDuckGo)",
-    description="Enter a company/topic. The app searches the web (DuckDuckGo) and uses Google's Gemini AI to write a case study *based only on the search results*. \n**Requires a correctly configured `GOOGLE_API_KEY` secret in Hugging Face Space Settings.**",
+    description="Enter a topic. The app searches the web (DDG) and uses Gemini AI to write a case study based *only* on the search results.\n**Requires `GOOGLE_API_KEY` secret in HF Space Settings.**",
     allow_flagging="never",
     examples=[
         ["How Spotify uses AI for music recommendations"],
         ["Tesla Autopilot development challenges"],
         ["Use of AI in drug discovery by Pfizer"],
-        ["Environmental impact reduction using AI at Google data centers"],
     ],
-    theme=gr.themes.Soft() # Optional: Apply a soft theme
+    theme=gr.themes.Soft()
 )
 
 print("🚀 Launching Gradio interface...")
-
-# Launch the interface (share=True is not needed on HF Spaces)
-# Use debug=True for more detailed logs initially, you can remove it later
 try:
-    iface.launch(debug=True)
+    # Removed debug=True for potentially cleaner logs in production, add back if needed
+    iface.launch()
 except Exception as e:
     print(f"❌ Failed to launch Gradio interface: {e}")
-    traceback.print_exc() # Log details in HF
+    traceback.print_exc()