Added user profile

app.py

@@ -7,143 +7,178 @@ 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=["Student", "Professor", "Administrator", "Other"])
+
+    if profile_selector == "Other":
+        profile = st.text_input("What is your role?")
+    else:
+        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 = "SUP'ASSISTANT")
+    st.subheader(f"Hello {st.session_state.name}! How can I help 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 retireved documents**", min_value=0, max_value=10, value=5, step=1)
+    if st.sidebar.button("Clear message history"):
+        print("Clearing message history")
+        memory.clear()
 
-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 = "SUP'ASSISTANT")
-st.subheader("Hey there! How can I help 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 retireved 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 about SUP'COM ?")
-
-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()}
-
-
-        with collect_runs() as cb:
-            for chunk in chain.stream(input_dict, config={"tags": ["SUP'ASSISTANT"]}):
-                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)
-
-        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}
-                ],
-                vectors=[
-                    question_embedding,
-                    answer_embedding,
-                ],
-                parallel=4,
-                max_retries=3,
-                )
+    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)
 
-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},
-    }
+    prompt = st.chat_input(placeholder="What do you need to know about SUP'COM ?")
 
-    # Get the score mapping based on the selected feedback option
-    scores = score_mappings[feedback_option]
+    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()}
 
-    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']}"
+            with collect_runs() as cb:
+                for chunk in chain.stream(input_dict, config={"tags": ["SUP'ASSISTANT"]}):
+                    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)
 
-            # 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"),
-                )
-                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)
+                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}
+                    ],
+                    vectors=[
+                        question_embedding,
+                        answer_embedding,
+                    ],
+                    parallel=4,
+                    max_retries=3,
+                    )
 
             
-            qdrant_client.upload_collection(            
-                collection_name="chat-history",
-                payload=[
-                    {"text": comment,"Score:":score, "type": "feedback", "question_ID": run_id}
-                ],
-                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,
+                    )