added params slider

app.py

@@ -1,3 +1,166 @@
+# 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
 from langchain.prompts import PromptTemplate
 from langchain_community.llms import CTransformers
@@ -9,7 +172,7 @@ 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()
@@ -68,25 +231,23 @@ st.title("🏥 Medicure RAG Chatbot")
 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")
 
+# Parameters section
+st.sidebar.header("Parameters")
+k_value = st.sidebar.slider("Number of relevant documents (k)", min_value=1, max_value=10, value=2)
+max_new_tokens = st.sidebar.slider("Max new tokens", min_value=64, max_value=1024, value=512)
+temperature = st.sidebar.slider("Temperature", min_value=0.1, max_value=1.0, value=0.8, step=0.1)
+
 # Initialize the chatbot components
 @st.cache_resource
-def initialize_chatbot():
-
+def initialize_chatbot(k, max_tokens, temp):
     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
+                        config={'max_new_tokens': max_tokens,
+                                'temperature': temp})
     
+    # initialize pinecone
     pc = Pinecone(api_key=PINECONE_API_KEY)
     index = pc.Index(index_name)
 
@@ -96,20 +257,34 @@ def initialize_chatbot():
     qa = RetrievalQA.from_chain_type(
         llm=llm,
         chain_type="stuff",
-        retriever=docsearch.as_retriever(search_kwargs={'k': 2}),
+        retriever=docsearch.as_retriever(search_kwargs={'k': k}),
         return_source_documents=True,
         chain_type_kwargs=chain_type_kwargs)
     return qa
 
-qa = initialize_chatbot()
+qa = initialize_chatbot(k_value, max_new_tokens, temperature)
 
 # Chat interface
-user_input = st.text_input("Ask your medical question:")
+user_input = st.text_input("Ask your question:")
 if st.button("Send", key="send"):
     if user_input:
-        with st.spinner("Thinking..."):
-            result = qa({"query": user_input})
-            response = result["result"]
+        # 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))
 
@@ -121,15 +296,6 @@ 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">