import gradio as gr
import pandas as pd
from serpapi import GoogleSearch

# SERP API key (replace with your actual key)
SERP_API_KEY = "785988650046bf6eddbc597cbf87330e2d53f8a3bacb4bac62a90ab1ecfa2445"

def search_and_answer(question):
    try:
        # Step 1: Fetch search results from Google using SERP API
        search_params = {
            "q": question,
            "hl": "en",
            "gl": "us",
            "api_key": SERP_API_KEY
        }
        search = GoogleSearch(search_params)
        results = search.get_dict()

        # Extract top 3 organic search results
        extracted_results = []
        for result in results.get("organic_results", [])[:3]:
            extracted_results.append({
                "title": result.get("title"),
                "link": result.get("link"),
                "snippet": result.get("snippet", "No description available.")
            })

        if not extracted_results:
            return pd.DataFrame(columns=["Answer", "Source", "Confidence Score"])

        # Step 2: Prepare final dataframe with sources and confidence scores
        data = []
        for i, res in enumerate(extracted_results):
            confidence_score = round(1 - (i * 0.2), 2)  # Simulated confidence score
            data.append({
                "Answer": res["snippet"],
                "Source": res["link"],
                "Confidence Score": confidence_score
            })

        df = pd.DataFrame(data)
        return df

    except Exception as e:
        return pd.DataFrame({"Error": [str(e)]})

# Step 3: Create Gradio Interface
iface = gr.Interface(
    fn=search_and_answer,
    inputs=gr.Textbox(label="Ask a Question"),
    outputs=gr.Dataframe(headers=["Answer", "Source", "Confidence Score"]),
    title="AI-Powered Q&A System ",
    description="Enter a question and get top 3 answers from web search with confidence scores."
)

# Launch the Gradio app with debug enabled
iface.launch(share=True, debug=True)