app.py

import gradio as gr
from search import search_google
from llm import generate_answer
from memory import ConversationMemory

# Initialize conversation memory
memory = ConversationMemory()

def ask_agent(question):
    # Retrieve conversation context
    context = memory.get_context()
    
    # Search for information
    search_results = search_google(question, num_results=5)
    
    if not search_results:
        return "I couldn't find any relevant information about that. Could you try rephrasing your question?"
    
    # Generate human-like response
    answer = generate_answer(
        question=question,
        context=context,
        search_results=search_results
    )
    
    # Update conversation history
    memory.add_exchange(question, answer)
    
    # Format response with sources
    formatted_response = f"""
🤖 **Assistant**: {answer['response']}
    
🔍 **Sources I used**:
"""
    for source in answer['sources']:
        formatted_response += f"- [{source['title']}]({source['url']})\n"
    
    return formatted_response

# Gradio chat interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🧠 **AI Research Assistant**")
    chatbot = gr.Chatbot(height=500)
    msg = gr.Textbox(label="Your Question")
    clear = gr.Button("Clear History")
    
    def respond(message, chat_history):
        bot_message = ask_agent(message)
        chat_history.append((message, bot_message))
        return "", chat_history
    
    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

demo.launch()