Update app.py

app.py

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+import streamlit as st
+from langchain import memory as lc_memory
+from langsmith import Client
+from streamlit_feedback import streamlit_feedback
+#from utils import get_expression_chain, retriever, get_embeddings, create_qdrant_collection
+from langchain_core.tracers.context import collect_runs
+from qdrant_client import QdrantClient
+from dotenv import load_dotenv
+import os
+if "access_granted" not in st.session_state:
+    st.session_state.access_granted = False
+if "profile" not in st.session_state:
+    st.session_state.profile = None
+if "name" not in st.session_state:
+    st.session_state.name = None
+if not st.session_state.access_granted:
+    # Profile input section
+    st.title("User Profile")
+    name = st.text_input("Name")
+    profile_selector = st.selectbox("Profile", options=["Patient", "Doctor"] )
+
+    profile = profile_selector
+    if profile and name:
+        d = False
+    else:
+        d = True
+
+    submission = st.button("Submit", disabled=d)
+
+    if submission:
+        st.session_state.profile = profile
+        st.session_state.name = name
+        st.session_state.access_granted = True  # Grant access to main app
+        st.rerun()  # Reload the app
+else:
+    load_dotenv()
+    profile = st.session_state.profile
+    client = Client()
+    qdrant_api=os.getenv("QDRANT_API_KEY")
+    qdrant_url=os.getenv("QDRANT_URL")
+    qdrant_client = QdrantClient(qdrant_url ,api_key=qdrant_api)
+    st.set_page_config(page_title = "MEDICAL CHATBOT")
+    st.subheader(f"Hello {st.session_state.name}! How can I assist you today!")
+
+    memory = lc_memory.ConversationBufferMemory(
+        chat_memory=lc_memory.StreamlitChatMessageHistory(key="langchain_messages"),
+        return_messages=True,
+        memory_key="chat_history",
+    )
+    st.sidebar.markdown("## Feedback Scale")
+    feedback_option = (
+        "thumbs" if st.sidebar.toggle(label="`Faces` ⇄ `Thumbs`", value=False) else "faces"
+    )
+
+    with st.sidebar:
+        model_name = st.selectbox("**Model**", options=["llama-3.1-70b-versatile","gemma2-9b-it","gemma-7b-it","llama-3.2-3b-preview", "llama3-70b-8192", "mixtral-8x7b-32768"])
+        temp = st.slider("**Temperature**", min_value=0.0, max_value=1.0, step=0.001)
+        n_docs = st.number_input("**Number of retrieved documents**", min_value=0, max_value=10, value=5, step=1)
+
+    if st.sidebar.button("Clear message history"):
+        print("Clearing message history")
+        memory.clear()
+
+    retriever = retriever(n_docs=n_docs)
+    # Create Chain
+    chain = get_expression_chain(retriever,model_name,temp)
+
+    for msg in st.session_state.langchain_messages:
+        avatar = "🐒" if msg.type == "ai" else None
+        with st.chat_message(msg.type, avatar=avatar):
+            st.markdown(msg.content)
+
+
+    prompt = st.chat_input(placeholder="What do you need to know in the medical field ?")
+
+    if prompt :
+        with st.chat_message("user"):
+            st.write(prompt)
+        
+        with st.chat_message("assistant", avatar="🐒"):
+            message_placeholder = st.empty()
+            full_response = ""
+            # Define the basic input structure for the chains
+            input_dict = {"input": prompt.lower()}
+            used_docs = retriever.get_relevant_documents(prompt.lower())
+
+            with collect_runs() as cb:
+                for chunk in chain.stream(input_dict, config={"tags": ["MEDICAL CHATBOT"]}):
+                    full_response += chunk.content
+                    message_placeholder.markdown(full_response + "▌")
+                memory.save_context(input_dict, {"output": full_response})
+                st.session_state.run_id = cb.traced_runs[0].id
+            message_placeholder.markdown(full_response)
+            if used_docs :
+                docs_content = "\n\n".join(
+                                        [
+                                            f"Doc {i+1}:\n"
+                                            f"Source: {doc.metadata['source']}\n"
+                                            f"Title: {doc.metadata['title']}\n"
+                                            f"Content: {doc.page_content}\n"
+                                            for i, doc in enumerate(used_docs)
+                                        ]
+                                    )
+                with st.sidebar:
+                    st.download_button(
+                    label="Consulted Documents",
+                    data=docs_content,
+                    file_name="Consulted_documents.txt",
+                    mime="text/plain",
+                )
+
+            with st.spinner("Just a sec! Dont enter prompts while loading pelase!"):
+                run_id = st.session_state.run_id
+                question_embedding = get_embeddings(prompt)
+                answer_embedding = get_embeddings(full_response)
+                # Add question and answer to Qdrant
+                qdrant_client.upload_collection(            
+                    collection_name="chat-history",
+                    payload=[
+                        {"text": prompt, "type": "question", "question_ID": run_id},
+                        {"text": full_response, "type": "answer", "question_ID": run_id, "used_docs":used_docs}
+                    ],
+                    vectors=[
+                        question_embedding,
+                        answer_embedding,
+                    ],
+                    parallel=4,
+                    max_retries=3,
+                    )
+
+            
+
+    if st.session_state.get("run_id"):
+        run_id = st.session_state.run_id
+        feedback = streamlit_feedback(
+            feedback_type=feedback_option,
+            optional_text_label="[Optional] Please provide an explanation",
+            key=f"feedback_{run_id}",
+        )
+
+        # Define score mappings for both "thumbs" and "faces" feedback systems
+        score_mappings = {
+            "thumbs": {"👍": 1, "👎": 0},
+            "faces": {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0},
+        }
+
+        # Get the score mapping based on the selected feedback option
+        scores = score_mappings[feedback_option]
+
+        if feedback:
+            # Get the score from the selected feedback option's score mapping
+            score = scores.get(feedback["score"])
+
+            if score is not None:
+                # Formulate feedback type string incorporating the feedback option
+                # and score value
+                feedback_type_str = f"{feedback_option} {feedback['score']}"
+
+                # Record the feedback with the formulated feedback type string
+                # and optional comment
+                with st.spinner("Just a sec! Dont enter prompts while loading pelase!"):
+                    feedback_record = client.create_feedback(
+                        run_id,
+                        feedback_type_str,
+                        score=score,
+                        comment=feedback.get("text"),
+                        source_info={"profile":profile}
+                    )
+                    st.session_state.feedback = {
+                        "feedback_id": str(feedback_record.id),
+                        "score": score,
+                    }
+            else:
+                st.warning("Invalid feedback score.")
+
+            with st.spinner("Just a sec! Dont enter prompts while loading pelase!"):
+                if feedback.get("text"):
+                    comment = feedback.get("text")
+                    feedback_embedding = get_embeddings(comment)
+                else:
+                    comment = "no comment"
+                    feedback_embedding = get_embeddings(comment)
+
+                
+                qdrant_client.upload_collection(            
+                    collection_name="chat-history",
+                    payload=[
+                        {"text": comment,
+                         "Score:":score, 
+                         "type": "feedback", 
+                         "question_ID": run_id, 
+                         "User_profile":profile}
+                    ],
+                    vectors=[
+                        feedback_embedding
+                    ],
+                    parallel=4,
+                    max_retries=3,
+                    )
+
+
+