Update app.py

app.py

@@ -5,6 +5,7 @@ from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_core.messages import HumanMessage
 import os
 import random
+import json  # <<< THE FIX IS HERE
 
 # --- Configuration ---
 GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
@@ -13,8 +14,6 @@ GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
 def fetch_market_chatter(niche_description: str) -> list[str]:
     """Simulates scraping Reddit, Hacker News, etc., for a given niche."""
     print(f"Simulating scraping for: {niche_description}")
-    # In a real app, this would use PRAW, snscrape, etc.
-    # We'll generate realistic-sounding comments.
     base_comments = [
         "Ugh, another project management tool that charges per user. I'm a solo founder, this kills me.",
         "I love the idea of Notion but it's just too slow and bloated now. I need something faster.",
@@ -32,46 +31,46 @@ def fetch_market_chatter(niche_description: str) -> list[str]:
 def analyze_resonance(niche_description: str, comments: list[str]) -> dict:
     """Uses an LLM to perform topic modeling, pain point extraction, and sentiment analysis."""
     if not GEMINI_API_KEY:
-        return {"error": "GEMINI_API_KEY not set."}
+        return {"error": "GEMINI_API_KEY not set in Space secrets."}
 
-    llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest", google_api_key=GEMINI_API_KEY)
-    
-    # Create a single, powerful prompt for the analysis
-    comments_formatted = "\n".join([f"- \"{c}\"" for c in comments])
-    prompt = f"""
-    You are a market research analyst with superhuman insight. Analyze the following raw market chatter (comments from Reddit, Hacker News, etc.) for a proposed product idea.
-
-    **Proposed Product Idea:** "{niche_description}"
-
-    **Raw Market Chatter:**
-    {comments_formatted}
-
-    **Your Task:**
-    Analyze the chatter and return a JSON object with the following structure. Do not include any text outside the JSON object.
-    {{
-      "pain_points": [
-        "A summary of the most common complaint or frustration.",
-        "A summary of the second most common complaint.",
-        "A summary of a third, more niche complaint."
-      ],
-      "magic_words": [
-        "A positive, benefit-oriented word or phrase people use.",
-        "Another evocative word people use to describe their ideal solution.",
-        "A third powerful, emotional word."
-      ],
-      "target_villain": "The name of a competitor or a type of product that people frequently complain about.",
-      "unserved_tribe": "A description of a specific user subgroup whose needs are not being met.",
-      "resonance_score": A number between 1 and 100 representing how well the proposed product idea fits the market chatter,
-      "resonance_justification": "A one-sentence explanation for the score you gave."
-    }}
-    """
     try:
+        llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest", google_api_key=GEMINI_API_KEY)
+        
+        comments_formatted = "\n".join([f"- \"{c}\"" for c in comments])
+        prompt = f"""
+        You are a market research analyst with superhuman insight. Analyze the following raw market chatter (comments from Reddit, Hacker News, etc.) for a proposed product idea.
+
+        **Proposed Product Idea:** "{niche_description}"
+
+        **Raw Market Chatter:**
+        {comments_formatted}
+
+        **Your Task:**
+        Analyze the chatter and return a valid JSON object with the following structure. Do not include any text, code block markers, or explanations outside the single JSON object.
+        {{
+          "pain_points": [
+            "A summary of the most common complaint or frustration.",
+            "A summary of the second most common complaint.",
+            "A summary of a third, more niche complaint."
+          ],
+          "magic_words": [
+            "A positive, benefit-oriented word or phrase people use.",
+            "Another evocative word people use to describe their ideal solution.",
+            "A third powerful, emotional word."
+          ],
+          "target_villain": "The name of a competitor or a type of product that people frequently complain about.",
+          "unserved_tribe": "A description of a specific user subgroup whose needs are not being met.",
+          "resonance_score": A number between 1 and 100 representing how well the proposed product idea fits the market chatter,
+          "resonance_justification": "A one-sentence explanation for the score you gave."
+        }}
+        """
         response = llm.invoke([HumanMessage(content=prompt)])
         # Clean up the response to ensure it's valid JSON
         json_str = response.content.strip().replace("```json", "").replace("```", "")
-        return json.loads(json_str)
+        return json.loads(json_str) # This line now works because `json` is imported
     except Exception as e:
-        return {"error": f"Failed to analyze resonance: {e}"}
+        # Catch JSON decoding errors specifically
+        return {"error": f"Failed to analyze resonance. The AI model may have returned an invalid format. Details: {e}"}
 
 # --- Main Orchestrator ---
 def run_precog_analysis(niche_description: str):
@@ -85,19 +84,18 @@ def run_precog_analysis(niche_description: str):
     # Stage 2 & 3
     analysis_result = analyze_resonance(niche_description, comments)
     if "error" in analysis_result:
-        yield f"Error: {analysis_result['error']}", None, None, gr.Button("Analyze", interactive=True)
+        # Display the error in the main report area for visibility
+        yield f"## Analysis Error\n\n**Details:** {analysis_result['error']}", "", "Error", gr.Button("Analyze Again", interactive=True)
         return
     
     # --- Prepare Outputs ---
-    
-    # The main report
     score = analysis_result.get('resonance_score', 0)
     color = "green" if score > 65 else "orange" if score > 40 else "red"
     report_md = f"""
     <div style="text-align:center; border: 2px solid {color}; border-radius:10px; padding:20px;">
         <h2 style="margin:0;">Market Resonance Score</h2>
         <p style="font-size: 80px; font-weight:bold; margin:0; color:{color};">{score}/100</p>
-        <p style="margin:0; font-style:italic;">"{analysis_result.get('resonance_justification', '')}"</p>
+        <p style="margin:0; font-style:italic;">"{analysis_result.get('resonance_justification', 'No justification provided.')}"</p>
     </div>
 
     ### 💥 Top 3 Unspoken Pain Points
@@ -112,18 +110,17 @@ def run_precog_analysis(niche_description: str):
     - `{analysis_result.get('magic_words', ['N/A'])[2]}`
     """
 
-    # The strategic insights
     strategy_md = f"""
     ### 🎯 Your Go-to-Market Strategy
     
     **Your "Villain":**
-    Position your product as the direct antidote to **{analysis_result.get('target_villain', 'N/A')}**. Your marketing should say, "Tired of [Villain's problem]? We fixed it."
+    Position your product as the direct antidote to **{analysis_result.get('target_villain', 'existing complex tools')}**. Your marketing should say, "Tired of [Villain's problem]? We fixed it."
 
     **Your Unserved "Tribe":**
-    Focus your initial launch on this niche group: **{analysis_result.get('unserved_tribe', 'N/A')}**. They are desperately looking for a solution and will become your first evangelists.
+    Focus your initial launch on this niche group: **{analysis_result.get('unserved_tribe', 'no specific tribe identified')}**. They are desperately looking for a solution and will become your first evangelists.
     """
 
-    yield report_md, strategy_md, "Analysis Complete", gr.Button("Analyze", interactive=True)
+    yield report_md, strategy_md, "Analysis Complete", gr.Button("Analyze Resonance", interactive=True)
 
 # --- Gradio UI ---
 with gr.Blocks(theme=gr.themes.Glass(), css=".gradio-container{max-width: 800px !important}") as demo:
@@ -144,7 +141,7 @@ with gr.Blocks(theme=gr.themes.Glass(), css=".gradio-container{max-width: 800px
             gr.Markdown("## Strategy")
             strategy_output = gr.Markdown()
     
-    status_output = gr.Markdown() # A simple status bar
+    status_output = gr.Markdown()
 
     submit_btn.click(
         fn=run_precog_analysis,
@@ -153,7 +150,7 @@ with gr.Blocks(theme=gr.themes.Glass(), css=".gradio-container{max-width: 800px
     )
 
     gr.Markdown("---")
-    gr.Markdown("### This is a demo of the Pre-Cog Engine. \n The Pro version provides access to live data streams, deeper analysis, and continuous market monitoring. \n **[🚀 Launching Soon - Inquire About Early Access](https://gumroad.com/)**")
+    gr.Markdown("### This is a demo of the Pre-Cog Engine. \n The Pro version provides access to live data streams, deeper analysis, and continuous market monitoring. \n **[🚀 Inquire About Early Access on Gumroad](https://gumroad.com/)**")
 
 if __name__ == "__main__":
     demo.launch()