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app.py

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+import streamlit as st
+from utils import final_function
+
+qa, memory = final_function()
+import streamlit as st
+import io
+import re
+import sys
+from typing import Any, Callable
+
+def capture_and_display_output(func: Callable[..., Any], args, **kwargs) -> Any:
+    # Capture the standard output
+    original_stdout = sys.stdout
+    sys.stdout = output_catcher = io.StringIO()
+
+    # Run the given function and capture its output
+    response = func(args, **kwargs)
+
+    # Reset the standard output to its original value
+    sys.stdout = original_stdout
+
+    # Clean the captured output
+    output_text = output_catcher.getvalue()
+    clean_text = re.sub(r"\x1b[.?[@-~]", "", output_text)
+
+    # Custom CSS for the response box
+    st.markdown("""
+    <style>
+        .response-value {
+            border: 2px solid #6c757d;
+            border-radius: 5px;
+            padding: 20px;
+            background-color: #f8f9fa;
+            color: #3d3d3d;
+            font-size: 20px;  # Change this value to adjust the text size
+            font-family: monospace;
+        }
+    </style>
+    """, unsafe_allow_html=True)
+
+    # Create an expander titled "See Verbose"
+    with st.expander("See Langchain Thought Process"):
+        # Display the cleaned text in Streamlit as code
+        st.code(clean_text)
+
+    return response
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+def chat_ui(qa):
+    # Accept user input
+    if prompt := st.chat_input(
+        "Ask me questions: How can I retrieve data from Deep Lake in Langchain?"
+    ):
+
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        # Display user message in chat message container
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            full_response = ""
+
+            # Load the memory variables, which include the chat history
+            memory_variables = memory.load_memory_variables({})
+
+            # Predict the AI's response in the conversation
+            with st.spinner("Searching course material"):
+                response = capture_and_display_output(
+                    qa, ({"question": prompt, "chat_history": memory_variables})
+                )
+
+            # Display chat response
+            full_response += response["answer"]
+            message_placeholder.markdown(full_response + "▌")
+            message_placeholder.markdown(full_response)
+
+            #Display top 2 retrieved sources
+            source = response["source_documents"][0].metadata
+            source2 = response["source_documents"][1].metadata
+            with st.expander("See Resources"):
+                st.write(f"Title: {source['title'].split('·')[0].strip()}")
+                st.write(f"Source: {source['source']}")
+                st.write(f"Relevance to Query: {source['relevance_score'] * 100}%")
+                st.write(f"Title: {source2['title'].split('·')[0].strip()}")
+                st.write(f"Source: {source2['source']}")
+                st.write(f"Relevance to Query: {source2['relevance_score'] * 100}%")
+
+        # Append message to session state
+        st.session_state.messages.append(
+            {"role": "assistant", "content": full_response}
+        )
+
+# Run function passing the ConversationalRetrievalChain
+
+chat_ui(qa)

requirements.txt

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+langchain
+langchain-community
+deeplake
+langchain-groq
+cohere
+apify-client 
+tiktoken
+python-dotenv
+langchain-groq
+langchain-cohere
+lark
+streamlit