app.py

import streamlit as st
from meta_ai_api import MetaAI

# Initialize Meta AI API
ai = MetaAI()

def fetch_response(query):
    response = ai.prompt(message=query)
    return response

def display_sources(sources):
    st.write("### Sources")
    for source in sources:
        st.markdown(f"[{source['title']}]({source['link']})")

def main():
    st.title("AI Response Analytics Tool")

    # User input
    user_query = st.text_area("Enter your query:", height=150)
    submit_button = st.button("Analyze Query")

    if submit_button and user_query:
        # Fetching response from Meta AI
        response = fetch_response(user_query)
        st.write("### AI Response")
        st.write(response['message'])

        # Display sources with clickable links
        if 'sources' in response:
            display_sources(response['sources'])

        # Here you might add further analysis of the response, such as:
        # - Text analysis for sentiment
        # - Keyword extraction
        # - Length of the response
        # - Etc.

        # Optionally, save the query and response for historical analysis
        # Implement data storage if needed

if __name__ == "__main__":
    main()