Create app.py

app.py

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+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()