import os
import gradio as gr
from google import genai
from google.genai.types import GenerateContentConfig, GoogleSearch, Tool

# Initialize GenAI Client
API_KEY = os.getenv("GOOGLE_API_KEY") 
client = genai.Client(api_key=API_KEY)
MODEL_ID = "gemini-2.5-flash" # Ensure this matches a valid model version

def google_search_query(question):
    try:
        # Define the Google Search Tool
        google_search_tool = Tool(google_search=GoogleSearch())
        
        instructions = "You are a helpful research assistant. Use a professional tone. " \
                       "Always cite your findings and be concise."

        # THE FIX: system_instruction goes INSIDE GenerateContentConfig
        response = client.models.generate_content(
            model=MODEL_ID,
            contents=question,
            config=GenerateContentConfig(
                tools=[google_search_tool],
                system_instruction=instructions  # Moved here
            ),
        )

        ai_response = response.text
        
        # Safely extract search results
        search_results = ""
        if response.candidates[0].grounding_metadata and response.candidates[0].grounding_metadata.search_entry_point:
            search_results = response.candidates[0].grounding_metadata.search_entry_point.rendered_content
            
        return ai_response, search_results
    except Exception as e:
        return f"Error: {str(e)}", ""

# Gradio Interface
app = gr.Interface(
    fn=google_search_query,
    inputs=gr.Textbox(lines=2, label="Ask a Question"),
    outputs=[
        gr.Textbox(label="AI Response"),
        gr.HTML(label="Search Results"),
    ],
    title="Professional Research Hub",
    description=(
        "Advanced AI assistant powered by Gemini 2.0 and Google Search. "
        "Ask complex questions and get grounded, real-time answers with verified citations."
    ),
    theme="soft"
)

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