app.py

import gradio as gr
import pandas as pd
import plotly.graph_objects as go
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage
import os
import random

# --- Configuration ---
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")

# --- Stage 1: Data Ingestion (Simulation) ---
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.",
        "Why can't any of these tools just have a simple, reliable integration with Google Calendar?",
        "I'm a writer, not a project manager. I just need a clean way to organize my chapters and research.",
        "Asana is too complex. Trello is too simple. Is there anything in between?",
        "The real magic would be a tool that automatically generates a weekly summary of my progress.",
        "Their customer support is a joke. Took three days to get a reply on a critical bug.",
        "The 'all-in-one' promise is a lie. It does 10 things poorly instead of one thing well.",
        "If someone built a beautiful, minimalist PM tool for visual artists, I'd pay $50/month in a heartbeat."
    ]
    return random.sample(base_comments, k=random.randint(5, len(base_comments)))

# --- Stage 2 & 3: AI Analysis Pipeline ---
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."}

    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:
        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)
    except Exception as e:
        return {"error": f"Failed to analyze resonance: {e}"}

# --- Main Orchestrator ---
def run_precog_analysis(niche_description: str):
    # This orchestrates the entire pipeline
    yield "Initializing Pre-Cog Engine...", None, None, gr.Button("Analyzing...", interactive=False)
    
    # Stage 1
    comments = fetch_market_chatter(niche_description)
    yield f"Scanning Market Chatter... ({len(comments)} data points found)", None, None, gr.Button("Analyzing...", interactive=False)

    # 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)
        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>
    </div>

    ### 💥 Top 3 Unspoken Pain Points
    1.  **{analysis_result.get('pain_points', ['N/A'])[0]}**
    2.  **{analysis_result.get('pain_points', ['N/A'])[1]}**
    3.  **{analysis_result.get('pain_points', ['N/A'])[2]}**

    ### ✨ Your "Magic Words" for Marketing
    Use these exact words in your landing page copy. This is how your customers talk.
    - `{analysis_result.get('magic_words', ['N/A'])[0]}`
    - `{analysis_result.get('magic_words', ['N/A'])[1]}`
    - `{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."

    **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.
    """

    yield report_md, strategy_md, "Analysis Complete", gr.Button("Analyze", interactive=True)

# --- Gradio UI ---
with gr.Blocks(theme=gr.themes.Glass(), css=".gradio-container{max-width: 800px !important}") as demo:
    gr.Markdown("# 🔮 Pre-Cog: The Market Resonance Engine")
    gr.Markdown("Stop guessing. Analyze real-time market chatter to see if your idea will succeed *before* you build it.")
    
    with gr.Column():
        niche_input = gr.Textbox(label="Describe Your Product Idea or Niche", placeholder="e.g., 'An AI-powered project management tool for solopreneurs' or 'A better way to learn guitar online'")
        submit_btn = gr.Button("Analyze Resonance", variant="primary")

    gr.Markdown("---")
    
    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("##  Resonance Report")
            report_output = gr.Markdown()
        with gr.Column(scale=1):
            gr.Markdown("## Strategy")
            strategy_output = gr.Markdown()
    
    status_output = gr.Markdown() # A simple status bar

    submit_btn.click(
        fn=run_precog_analysis,
        inputs=[niche_input],
        outputs=[report_output, strategy_output, status_output, submit_btn]
    )

    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/)**")

if __name__ == "__main__":
    demo.launch()