app.py

import gradio as gr
import google.generativeai as genai
from ddgs import DDGS
import os # Required to read the Hugging Face Secret

# --- PROFESSIONAL DATA RETRIEVAL ---
def get_professional_data(options_list):
    collected_data = []
    try:
        with DDGS() as ddgs:
            comp_query = f"professional comparison review {', '.join(options_list)} 2024 2025 benchmarks"
            comp_results = list(ddgs.text(comp_query, max_results=5))
            for r in comp_results:
                collected_data.append(f"[Comparison Source]: {r['body']}")
                
            for opt in options_list:
                tech_query = f"{opt} technical specifications pros and cons professional analysis"
                tech_results = list(ddgs.text(tech_query, max_results=3))
                for r in tech_results:
                    collected_data.append(f"[Technical Source - {opt}]: {r['body']}")
    except Exception as e:
        return f"Search connection error: {e}"
                
    return "\n".join(collected_data)

# --- MAIN PROCESSING FUNCTION ---
def decide_best_option(options_input, use_web):
    # 1. GET KEY DIRECTLY FROM SECRETS (No UI input needed)
    api_key = os.getenv("GOOGLE_API_KEY")
    
    if not api_key:
        return "❌ System Error: API Key not found in environment secrets.", ""
    if not options_input.strip():
        return "⚠️ Warning: Please enter at least one option.", ""

    try:
        genai.configure(api_key=api_key)
        
        try:
            model = genai.GenerativeModel('gemma-4-31b-it')
        except:
            available = [m.name for m in genai.list_models() if 'gemma' in m.name.lower()]
            model = genai.GenerativeModel(available[0] if available else 'gemini-1.5-flash')

        options_list = [o.strip() for o in options_input.split('\n') if o.strip()]
        
        web_data = ""
        if use_web:
            web_data = get_professional_data(options_list)

        prompt = f"""
        You are Gemma 4 31B IT, a professional analytical expert.
        OPTIONS TO COMPARE: {options_input}
        RESEARCH DATA: {web_data if web_data else "No external data provided."}
        INSTRUCTIONS: 1. Analyze Research Data. 2. Focus on specs/benchmarks. 3. Select absolute winner.
        OUTPUT FORMAT:
        ANALYSIS: [Detailed professional paragraph]
        WINNER: [Only the name of the chosen option]
        """

        response = model.generate_content(prompt)
        res_text = response.text
        
        keyword = "WINNER:"
        if keyword in res_text:
            parts = res_text.split(keyword)
            winner = parts[-1].strip()
            analysis = " ".join(parts[:-1]).replace("ANALYSIS:", "").strip()
        else:
            winner = "Could not determine winner"
            analysis = res_text.strip()

        return winner, analysis

    except Exception as e:
        return f"System Error: {e}", ""

# --- GRADIO UI DESIGN ---
custom_css = """
#winner-box { 
    font-size: 32px !important; 
    font-weight: bold !important; 
    text-align: center !important; 
    color: #1E293B !important; 
    border: 4px solid #E2E8F0 !important; 
    border-radius: 15px !important;
    background-color: #FFFFFF !important;
}
#analysis-box { 
    text-align: left !important; 
    font-style: italic !important; 
    color: #475569 !important; 
}
"""

with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🎯 Gemma 4 Decision Engine")
    gr.Markdown("High-accuracy choices based on technical research and Gemma 4 31B IT.")
    
    with gr.Row():
        with gr.Column(scale=1):
            # API KEY TEXTBOX REMOVED FROM HERE
            search_checkbox = gr.Checkbox(label="Enable Web Research 🌐", value=True)
            options_input = gr.Textbox(
                label="Enter Options (One per line)", 
                placeholder="iPhone 16 Pro\nSamsung S24 Ultra\nPixel 9 Pro", 
                lines=7
            )
            submit_btn = gr.Button("Determine Best Option", variant="primary")
            
        with gr.Column(scale=1):
            winner_output = gr.Textbox(
                label="🏆 THE WINNER", 
                elem_id="winner-box", 
                interactive=False
            )
            analysis_output = gr.Markdown(
                label="📄 Professional Analysis", 
                elem_id="analysis-box", 
                value="Analysis will appear here..."
            )

    # Trigger updated to only send options and checkbox
    submit_btn.click(
        fn=decide_best_option, 
        inputs=[options_input, search_checkbox], 
        outputs=[winner_output, analysis_output]
    )

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