Create app.py

app.py

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+# Step 1: Install/Update necessary libraries
+# Using -U ensures the latest version of google-generativeai is installed
+#!pip install -q -U gradio duckduckgo_search google-generativeai python-dotenv
+
+# --- IMPORTANT: After this cell runs, RESTART THE RUNTIME ---
+# --- (Runtime -> Restart runtime) for the library update to take effect ---
+
+# Step 2: Import libraries
+import gradio as gr
+import google.generativeai as genai
+from duckduckgo_search import DDGS
+import os
+import textwrap
+from google.colab import userdata # For Colab secrets
+import traceback # For detailed error logging if needed
+
+# Step 3: Configure API Key (Using Colab Secrets)
+# --- Instructions ---
+# 1. Go to the "Secrets" tab (key icon 🔑) on the left pane in Colab.
+# 2. Click "+ Add a new secret".
+# 3. Set the NAME exactly as: GOOGLE_API_KEY
+# 4. Paste your actual Google AI API key into the VALUE field.
+# 5. Ensure the "Notebook access" toggle is ON for this secret.
+# 6. Do NOT paste your API key directly into this code.
+
+# Initialize flag and key variable
+is_api_configured = False
+GOOGLE_API_KEY = None
+
+print("⚙️ Attempting to configure Google API Key...")
+try:
+    GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY')
+
+    if GOOGLE_API_KEY:
+        genai.configure(api_key=GOOGLE_API_KEY)
+        print("✅ Google API Key configured successfully.")
+        is_api_configured = True # Set flag to True ONLY if configure() succeeds
+    else:
+        # Secret found but empty - treat as not configured
+        print("⚠️ Error: GOOGLE_API_KEY secret found but is empty.")
+        is_api_configured = False
+
+except userdata.SecretNotFoundError:
+    print("❌ Error: Secret 'GOOGLE_API_KEY' not found.")
+    print("➡️ Please go to 'Secrets' (key icon 🔑) and add GOOGLE_API_KEY with your API key value.")
+    is_api_configured = False # Not configured if secret not found
+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() # Uncomment for detailed traceback during configuration
+
+# --- End of API Key Configuration ---
+
+
+# Step 4: Define Helper Functions
+
+# Function to perform web search
+def search_web(query, num_results=7):
+    """Searches the web using DuckDuckGo and returns formatted results."""
+    print(f"🔍 Searching the web for: '{query}'...")
+    try:
+        with DDGS() as ddgs:
+            # Using region='wt-wt' for potentially broader results
+            results = list(ddgs.text(query, region='wt-wt', safesearch='off', max_results=num_results)) # Added safesearch='off'
+            if not results:
+                print("⚠️ No search results found.")
+                return "No relevant search results found for the query."
+
+            # Format results for the LLM
+            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"
+                context += f"   URL: {result.get('href', 'N/A')}\n"
+                snippet = result.get('body', 'N/A')
+                context += f"   Snippet: {snippet}\n\n"
+
+            print(f"✅ Found {len(results)} results.")
+            return context
+    except Exception as e:
+        print(f"❌ Error during web search: {e}")
+        # traceback.print_exc() # Uncomment for detailed traceback
+        return f"Error occurred during web search. Details: {e}"
+
+# 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 ---
+    if not is_api_configured:
+         print("❌ Cannot generate: Google API Key not configured successfully.")
+         return "Error: Google API Key not configured successfully. Please check Colab Secrets setup and restart runtime."
+
+    # --- Check 2: Search Results ---
+    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 ---
+    model_name = 'gemini-1.5-flash-latest' # Using the recommended modern model
+    # model_name = 'gemini-1.0-pro' # Alternative if flash causes issues
+    # model_name = 'gemini-pro' # Less likely to work based on previous error
+    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()
+        # Try to list available models if initialization fails
+        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 not available_models:
+                 return f"Error setting up the AI model: {e}. Additionally, no compatible models were found via ListModels."
+            else:
+                 # Suggest trying one of the listed models
+                 suggested_model = next((m for m in available_models if 'flash' in m or 'pro' in m), available_models[0]) # Simple suggestion logic
+                 return f"Error setting up the AI model '{model_name}': {e}. You could try using one of the available models like: '{suggested_model.split('/')[-1]}'"
+
+        except Exception as list_e:
+             print(f"   Additionally failed to list available models: {list_e}")
+             return f"Error setting up the AI model '{model_name}': {e}. Failed to list alternatives."
+
+
+    # --- Define the Prompt ---
+    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}"
+
+    Use the provided search results as your *only* source of information. Synthesize the information into a well-structured case study.
+
+    **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 (challenge, initiative). 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*. Describe technology use (like GenAI) if mentioned.
+    **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*. Describe qualitative benefits mentioned. If no results are mentioned, state that.
+    **8. Conclusion:** Summarize key takeaways *based on the provided information*. Reiterate challenge, solution, outcomes *from sources*.
+    **9. Sources:** List the URLs (`URL:` lines) from the search results that were most relevant.
+
+    **Instructions:**
+    *   Adhere strictly to the format above. Use Markdown `##` for section headings (e.g., `## 1. Title`).
+    *   Base your writing ***exclusively*** on the information in the "Provided Search Context". Do not invent information or use external knowledge.
+    *   If details for a section are missing in the sources, explicitly state: "Information not available in the provided sources." Do not leave sections blank.
+    *   Maintain an objective and professional tone.
+    *   Ensure coherence and logical flow.
+    *   Format the output using Markdown.
+
+    **Provided Search Context:**
+    ---
+    {search_context}
+    ---
+
+    Now, please generate the case study for "{topic}".
+    """
+
+    # --- Generate Content ---
+    try:
+        # Optional: Add safety settings if needed
+        # safety_settings = [...]
+        # response = model.generate_content(prompt, safety_settings=safety_settings)
+        response = model.generate_content(prompt)
+
+        # --- Process Response Safely ---
+        # Check for content parts first
+        if response.parts:
+            generated_text = "".join(part.text for part in response.parts)
+            print("✅ Case study generated successfully.")
+            return generated_text
+        # Check for blocking reasons if no parts exist
+        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."
+        # Handle candidates being empty or other unexpected scenarios
+        elif not response.candidates:
+             finish_reason = response.candidates[0].finish_reason if response.candidates else "UNKNOWN" # Attempt to get finish reason
+             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}). This might indicate an issue with the prompt, the model, or safety filters."
+        else:
+             # Fallback for other unexpected empty response scenarios
+             print("⚠️ Warning: Generation finished but produced no text content for unknown reasons.")
+             # You could inspect the raw response object here if needed: print(response)
+             return "Error: The AI model generated an empty response. This might be due to the input, content filters, or a temporary issue."
+
+
+    except Exception as e:
+        print(f"❌ Error during case study generation: {e}")
+        # traceback.print_exc() # Uncomment for detailed traceback
+        error_message = f"An unexpected error occurred during AI generation: {e}"
+        # Check for common API errors
+        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 verify your GOOGLE_API_KEY secret in Colab and ensure the Gemini API is enabled in your Google Cloud project."
+        elif "Model not found" in str(e) or "models/" in str(e) and "is not found" in str(e):
+             # Make error more specific based on the original error message
+             error_message = f"Error: The specified AI model ('{model_name}') was not found or is not supported for generateContent with the current API version. Check the model name or try updating the library (`!pip install -U google-generativeai` + restart runtime)."
+             # Attempt to list models again here might be useful
+             try:
+                 available_models = [m.name for m in genai.list_models() if 'generateContent' in m.supported_generation_methods]
+                 error_message += f"\n   Available models supporting generateContent: {available_models}"
+             except Exception:
+                 error_message += "\n   Failed to retrieve list of available models."
+
+        elif "Resource has been exhausted" in str(e) or "Quota" in str(e):
+             error_message = "Error: API quota exceeded. Please check your usage limits in Google Cloud Console or wait and try again."
+        elif "Invalid API key" in str(e):
+             error_message = "Error: The provided API key is invalid. Please double-check the GOOGLE_API_KEY secret value."
+        elif hasattr(e, 'message'): # General Google API error message structure
+             # Append original message if available and different
+             if str(e) != e.message:
+                 error_message = f"Error during AI generation: {e.message} (Details: {e})"
+             else:
+                 error_message = f"Error during AI generation: {e.message}"
+
+
+        return error_message
+
+
+# Step 5: Define the main processing function for Gradio
+def create_case_study(company_or_topic):
+    """Orchestrates the web search and case study generation process."""
+    print("-" * 60) # Separator for new request
+    if not company_or_topic or not company_or_topic.strip():
+        print("⚠️ Input validation failed: Empty topic received.")
+        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)
+    case_study_markdown = generate_case_study(cleaned_topic, search_results_context)
+
+    print("-" * 60) # Separator for end of request
+    return case_study_markdown
+
+# Step 6: Create and Launch the Gradio Interface
+print("\n⚙️ Setting up Gradio interface...")
+
+# Add a final check before launching Gradio
+if not is_api_configured:
+    print("\n" + "="*60)
+    print("‼️ WARNING: Google API Key not configured successfully. ‼️")
+    print("   The Gradio interface will launch, but case study generation WILL FAIL.")
+    print("   Please ensure the 'GOOGLE_API_KEY' secret is correctly set up in Colab")
+    print("   and that you have restarted the runtime after any library updates.")
+    print("="*60 + "\n")
+    # Optionally, prevent launch entirely:
+    # print("\n❌ ERROR: Google API Key not configured. Cannot launch Gradio interface.")
+    # exit() # Uncomment to stop execution
+
+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')",
+        label="Company Name or Topic"
+    ),
+    outputs=gr.Markdown( # Use Markdown output for better formatting
+        label="Generated Case Study",
+        # line_breaks=True # Uncomment if you prefer single newlines to create <br> tags
+    ),
+    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` in Colab Secrets (🔑) and runtime restart after library updates.**",
+    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
+)
+
+print("🚀 Launching Gradio interface...")
+# Launch in debug mode for detailed logs, share=True creates a public link (useful in Colab)
+# Set share=False if you don't need a public link
+try:
+    iface.launch(debug=True, share=True)
+except Exception as e:
+    print(f"❌ Failed to launch Gradio interface: {e}")
+    print("   This might be due to networking issues, Colab limitations, or conflicts. Check logs.")
+    # traceback.print_exc() # Uncomment for detailed traceback on launch failure