updated thinking anim

app.py

@@ -1,164 +1,3 @@
-# import streamlit as st
-# from langchain.prompts import PromptTemplate
-# from langchain_community.llms import CTransformers
-# from src.helper import download_hf_embeddings, text_split, download_hf_model
-# from langchain_community.vectorstores import Pinecone as LangchainPinecone
-# import os
-# from dotenv import load_dotenv
-# from src.prompt import prompt_template
-# from langchain.chains import RetrievalQA
-# import time
-# from pinecone import Pinecone
-# from tqdm.auto import tqdm
-
-# # Load environment variables
-# load_dotenv()
-
-# PINECONE_API_KEY = os.getenv('PINECONE_API_KEY')
-# index_name = "medicure-chatbot"
-
-# # Set page configuration
-# st.set_page_config(page_title="Medical Chatbot", page_icon="🏥", layout="wide")
-
-# # Custom CSS for styling
-# st.markdown("""
-# <style>
-#     .stApp {
-#         background-color: #f0f8ff;
-#     }
-#     .stButton>button {
-#         background-color: #4CAF50;
-#         color: white;
-#         border-radius: 20px;
-#         border: none;
-#         padding: 10px 20px;
-#         transition: all 0.3s ease;
-#     }
-#     .stButton>button:hover {
-#         background-color: #333;
-#         transform: scale(1.05);
-#         color:#fff;
-#     }
-#     .footer {
-#         position: fixed;
-#         left: 0;
-#         bottom: 0;
-#         width: 100%;
-#         background-color: #333;
-#         color: white;
-#         text-align: center;
-#         padding: 10px 0;
-#     }
-#     .social-icons a {
-#         color: white;
-#         margin: 0 10px;
-#         font-size: 24px;
-#     }
-# </style>
-# """, unsafe_allow_html=True)
-
-# # Initialize session state for chat history
-# if 'chat_history' not in st.session_state:
-#     st.session_state.chat_history = []
-
-# # Header
-# st.title("🏥 Medicure RAG Chatbot")
-
-# # Display welcome message
-# st.write("Welcome to Medicure Chatbot! Ask any medical question and I'll do my best to help you.")
-# st.write("#### Built with 🤗 Ctransformers, Langchain, and Pinecone. Powered by Metal-llama2-7b-chat quantized LLM")
-
-# # Initialize the chatbot components
-# @st.cache_resource
-# def initialize_chatbot():
-
-#     embeddings = download_hf_embeddings()
-#     # model_name_or_path = "TheBloke/Llama-2-7B-Chat-GGML"
-#     # model_basename = "llama-2-7b-chat.ggmlv3.q4_0.bin"
-#     # model_path = download_hf_model(model_name_or_path, model_basename)
-#     model_path = "TheBloke/Llama-2-7B-Chat-GGML"
-#     llm = CTransformers(model=model_path,
-#                         model_type="llama",
-#                         config={'max_new_tokens': 512,
-#                                 'temperature': 0.8})
-
-       
-    
-    
-#     # initiaize pinecone
-    
-#     pc = Pinecone(api_key=PINECONE_API_KEY)
-#     index = pc.Index(index_name)
-
-#     PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
-#     chain_type_kwargs = {"prompt": PROMPT}
-#     docsearch = LangchainPinecone(index, embeddings.embed_query, "text")
-#     qa = RetrievalQA.from_chain_type(
-#         llm=llm,
-#         chain_type="stuff",
-#         retriever=docsearch.as_retriever(search_kwargs={'k': 2}),
-#         return_source_documents=True,
-#         chain_type_kwargs=chain_type_kwargs)
-#     return qa
-
-# qa = initialize_chatbot()
-
-# # Chat interface
-# user_input = st.text_input("Ask your question:")
-# if st.button("Send", key="send"):
-#     if user_input:
-#         # Create a placeholder for the progress bar
-#         progress_placeholder = st.empty()
-        
-#         # Simulate progress with tqdm
-#         total_steps = 100
-#         with tqdm(total=total_steps, file=progress_placeholder, desc="Thinking", bar_format='{l_bar}{bar}') as pbar:
-#             for i in range(total_steps):
-#                 time.sleep(0.05)  # Adjust this value to control the speed of the progress bar
-#                 pbar.update(1)
-        
-#         # Get the actual response
-#         result = qa({"query": user_input})
-#         response = result["result"]
-        
-#         # Clear the progress bar
-#         progress_placeholder.empty()
-        
-#         st.session_state.chat_history.append(("You", user_input))
-#         st.session_state.chat_history.append(("Bot", response))
-
-# # Display chat history
-# st.subheader("Chat History")
-# for role, message in st.session_state.chat_history:
-#     if role == "You":
-#         st.markdown(f"**You:** {message}")
-#     else:
-#         st.markdown(f"**Bot:** {message}")
-
-# # Animated loading for visual appeal
-# def load_animation():
-#     with st.empty():
-#         for i in range(3):
-#             for j in ["⋅", "⋅⋅", "⋅⋅⋅", "⋅⋅⋅⋅"]:
-#                 st.write(f"Loading{j}")
-#                 time.sleep(0.2)
-#             st.write("")
-
-# # Footer with social links
-# st.markdown("""
-# <div class="footer">
-#     <div class="social-icons">
-#         <a href="https://github.com/4darsh-Dev" target="_blank"><i class="fab fa-github"></i></a>
-#         <a href="https://linkedin.com/in/adarsh-maurya-dev" target="_blank"><i class="fab fa-linkedin"></i></a>
-#         <a href="https://adarshmaurya.onionreads.com" target="_blank"><i class="fas fa-globe"></i></a>
-#         <a href="https://www.kaggle.com/adarshm09" target="_blank"><i class="fab fa-kaggle"></i></a>
-#     </div>
-#     <p> <p style="text-align:center;">Made with ❤️ by <a href="https://www.adarshmaurya.onionreads.com">Adarsh Maurya</a></p> </p>
-# </div>
-# """, unsafe_allow_html=True)
-
-# # Load Font Awesome for icons
-# st.markdown('<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.1/css/all.min.css">', unsafe_allow_html=True)
 
 
 import streamlit as st
@@ -271,23 +110,11 @@ qa = initialize_chatbot(k_value, max_new_tokens, temperature)
 # Chat interface
 user_input = st.text_input("Ask your question:")
 if st.button("Send", key="send"):
+
     if user_input:
-        # Create a progress bar
-        progress_bar = st.progress(0)
-        
-        
-        total_steps = 100
-        for i in range(total_steps):
-            time.sleep(0.05)  
-            progress_bar.progress((i + 1) / total_steps)
-        
-        # Get the actual response
-        result = qa({"query": user_input})
-        response = result["result"]
-        
-        # Clear the progress bar
-        progress_bar.empty()
-        
+        with st.spinner("Thinking..."):
+            result = qa({"query": user_input})
+            response = result["result"]
         st.session_state.chat_history.append(("You", user_input))
         st.session_state.chat_history.append(("Bot", response))
 
@@ -299,6 +126,17 @@ for role, message in st.session_state.chat_history:
     else:
         st.markdown(f"**Bot:** {message}")
 
+
+# Animated loading for visual appeal
+def load_animation():
+    with st.empty():
+        for i in range(3):
+            for j in ["⋅", "⋅⋅", "⋅⋅⋅", "⋅⋅⋅⋅"]:
+                st.write(f"Loading{j}")
+                time.sleep(0.2)
+            st.write("")
+
+
 # Footer with social links
 st.markdown("""
 <div class="footer">