Update app.py

app.py

@@ -7,199 +7,174 @@ 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)
+    
+load_dotenv()
+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("Hello! 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="Describe your symptoms or medical questions ?")
+
+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)
+                                ]
+                            )
 
-    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)
+            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,
+                )
 
-    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)
+        
 
+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}",
+    )
 
-    prompt = st.chat_input(placeholder="Describe your symptoms or medical questions ?")
+    # Define score mappings for both "thumbs" and "faces" feedback systems
+    score_mappings = {
+        "thumbs": {"👍": 1, "👎": 0},
+        "faces": {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0},
+    }
 
-    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",
-                )
+    # 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!"):
-                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,
-                    )
+                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,
+                )
 
-    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,
-                    )
-
-
-                
+