Made UI changes

app.py

@@ -29,6 +29,7 @@ st.set_page_config(page_title= "Medical chatbot", page_icon=":bot:")
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = []
 
+
 PINECONE_API_KEY = "1bae0d8e-019e-4e87-8080-ecf523e5f25f"
 def get_response(user_query):
     # Initilize the prompt
@@ -64,14 +65,7 @@ def get_response(user_query):
     # answer = vector_store.similarity_search(user_query, k=3)
     # return answer.stream().get("answer")
 
-# Function to simulate typing effect
-def type_effect(text):
-    for char in text:
-        st.write(char)
-        time.sleep(0.05)
-    st.write("")
 
-    import streamlit as st
 
 # Sidebar content
 with st.sidebar:
@@ -79,14 +73,18 @@ with st.sidebar:
     st.markdown("""
     Welcome to our Medical Chatbot project, an AI-powered application designed to assist users with their medical queries through intelligent interactions.
     """)
+    st.header('⚠️ Disclaimer:')
+    st.markdown("""
+    **Note**: This chatbot provides general medical info and is not a substitute for professional medical advice. Always consult your doctor for any health concerns. Do not delay seeking medical advice because of information from this app.
+    """)
 
     st.header('🔑 Key Features:')
     st.markdown("""
-    - **Custom LLM Model**: Utilizes a custom Large Language Model hosted on Hugging Face for tailored medical responses.
-    - **Hugging Face Integration**: Secure access using Hugging Face credentials for model authentication.
-    - **Streamlit Interface**: User-friendly and intuitive interface built with Streamlit.
-    - **LangChain for Prompt Management**: Ensures precise and contextually appropriate responses by managing prompts and chat history.
-    - **Pinecone for Vector Storage**: Efficient vector storage and retrieval for quick access to relevant information.
+    - 👉**Custom LLM Model**: Utilizes a custom Large Language Model hosted on Hugging Face for tailored medical responses.
+    - 👉**Hugging Face Integration**: Secure access using Hugging Face credentials for model authentication.
+    - 👉**Streamlit Interface**: User-friendly and intuitive interface built with Streamlit.
+    - 👉**LangChain for Prompt Management**: Ensures precise and contextually appropriate responses by managing prompts and chat history.
+    - 👉**Pinecone for Vector Storage**: Efficient vector storage and retrieval for quick access to relevant information.
     """)
 
     st.header('🚀 Future Enhancements:')
@@ -95,20 +93,35 @@ with st.sidebar:
     - **Enhanced Medical Knowledge Base**: Continuous updates to keep the model current with the latest medical information.
     """)
 
-    st.header('⚠️ Disclaimer:')
+    st.header(':building_construction: View Other projects')
     st.markdown("""
-    **Please Note**: This chatbot is designed to provide general medical information and is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health providers with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read on this application.
+    - 👉Github: https://github.com/Aniket2021448/
+    - 👉Portfolio: https://aniket2021448.github.io/My-portfolio/ 
     """)
 
+    st.header('🤗💬 Contact the developer')
     st.markdown("""
-    This project aims to provide a reliable, accessible, and user-friendly platform for preliminary medical consultations. Explore the features and provide feedback to help us improve. Thank you for being a part of our journey to revolutionize medical assistance with AI!
+    - 👉Email: https://github.com/Aniket2021448/
+    - 👉Portfolio: https://aniket2021448.github.io/My-portfolio/ 
     """)
 
 
-st.title("Medical chatbot")
+st.title("Medical chatbot 🤖")
 
 st.write("Welcome to the medical chatbot. Please enter your symptoms below and I will try to help you.")
 
+if "messages" not in st.session_state.keys():
+    st.session_state.chat_history.append({"bot": "How may I help you?"})
+
+if "chat_history" in st.session_state:
+    for message in st.session_state.chat_history:
+        if "bot" in message:
+            with st.chat_message("AI"):
+                st.markdown(message["bot"])
+
+
+st.session_state.chat_history = []
+
 if "chat_history" in st.session_state:
     for message in st.session_state.chat_history:
         if "user" in message:
@@ -118,6 +131,8 @@ if "chat_history" in st.session_state:
             with st.chat_message("AI"):
                 st.markdown(message["bot"])
 
+
+
 user_query = st.chat_input("Enter your symptoms here")
 if user_query is not None and user_query != "":
 
@@ -127,22 +142,13 @@ if user_query is not None and user_query != "":
         st.session_state.chat_history.append({"user": user_query})
 
     with st.chat_message("AI"):
-        # =""
-        # for message in st.session_state.chat_history:
-        #     if "user" in message:
-        #        += f"User: {message['user']}\n"
-        #     elif "bot" in message:
-        #        += f"Bot: {message['bot']}\n"
-
-        ai_response = get_response(user_query)
-        # st.write(type(ai_response))
-        result = ai_response["result"]
-        # type_effect(result)
-        st.markdown(result)
-
-        # Get the response from backend and present it here
-        st.session_state.chat_history.append({"bot": result})
 
+        with st.spinner("Thinking..."):
+            ai_response = get_response(user_query)
+            result = ai_response["result"]
+            
+            st.markdown(result)
+            st.session_state.chat_history.append({"bot": result})
 
 
 # import os