Upload 4 files

README.md

@@ -1,13 +1,2 @@
----
-title: CosmicNexus
-emoji: 😻
-colorFrom: yellow
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.28.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# medbot
+A chatbot that summarizes the patient's report and let them chat on the data

app.py

@@ -0,0 +1,113 @@
+import streamlit as st
+from streamlit_chat import message
+from langchain.chains import ConversationalRetrievalChain
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import LlamaCpp
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.document_loaders import PyPDFLoader
+import os
+import tempfile
+
+
+
+
+def initialize_session_state():
+    if 'history' not in st.session_state:
+        st.session_state['history'] = []
+
+    if 'generated' not in st.session_state:
+        st.session_state['generated'] = ["Hello! Ask me anything about 🤗"]
+
+    if 'past' not in st.session_state:
+        st.session_state['past'] = ["Hey! 👋"]
+
+def conversation_chat(query, chain, history):
+    result = chain({"question": query, "chat_history": history})
+    history.append((query, result["answer"]))
+    return result["answer"]
+
+def display_chat_history(chain):
+    reply_container = st.container()
+    container = st.container()
+
+    with container:
+        with st.form(key='my_form', clear_on_submit=True):
+            user_input = st.text_input("Question:", placeholder="Ask about your PDF", key='input')
+            submit_button = st.form_submit_button(label='Send')
+
+        if submit_button and user_input:
+            with st.spinner('Generating response...'):
+                output = conversation_chat(user_input, chain, st.session_state['history'])
+
+            st.session_state['past'].append(user_input)
+            st.session_state['generated'].append(output)
+
+    if st.session_state['generated']:
+        with reply_container:
+            for i in range(len(st.session_state['generated'])):
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
+                message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
+
+def create_conversational_chain(vector_store):
+    # Create llm
+    llm = LlamaCpp(
+    streaming = True,
+    model_path="mistral-7b-instruct-v0.1.Q2_K.gguf",
+    temperature=0.75,
+    top_p=1, 
+    verbose=True,
+    n_ctx=4096
+)
+    
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+    chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
+                                                 retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
+                                                 memory=memory)
+    return chain
+
+def main():
+    # Initialize session state
+    initialize_session_state()
+    st.title("MedReport Summarizer:")
+    # Initialize Streamlit
+    st.sidebar.title("Document Processing")
+    uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True)
+
+
+    if uploaded_files:
+        text = []
+        for file in uploaded_files:
+            file_extension = os.path.splitext(file.name)[1]
+            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+                temp_file.write(file.read())
+                temp_file_path = temp_file.name
+
+            loader = None
+            if file_extension == ".pdf":
+                loader = PyPDFLoader(temp_file_path)
+
+            if loader:
+                text.extend(loader.load())
+                os.remove(temp_file_path)
+
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=20)
+        text_chunks = text_splitter.split_documents(text)
+
+        # Create embeddings
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", 
+                                           model_kwargs={'device': 'cpu'})
+
+        # Create vector store
+        vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
+
+        # Create the chain object
+        chain = create_conversational_chain(vector_store)
+
+        
+        display_chat_history(chain)
+
+if __name__ == "__main__":
+    main()

off_load.py

@@ -0,0 +1,163 @@
+import streamlit as st
+import logging
+from streamlit_chat import message
+from langchain.chains import ConversationalRetrievalChain
+from langchain.embeddings import HuggingFaceEmbeddings, CacheBackedEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.llms import LlamaCpp
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader
+from langchain.storage import LocalFileStore
+from langchain.llms import HuggingFaceHub
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+
+from datetime import datetime
+import os
+import tempfile
+import requests  # Import requests here
+
+now = datetime.now()
+underlying_embeddings = HuggingFaceEmbeddings()
+
+
+
+def initialize_session_state():
+    if 'history' not in st.session_state:
+        st.session_state['history'] = []
+
+    if 'generated' not in st.session_state:
+        st.session_state['generated'] = ["Hello! Ask me anything about 🤗"]
+
+    if 'past' not in st.session_state:
+        st.session_state['past'] = ["Hey! 👋"]
+
+def conversation_chat(query, chain, history):
+    result = chain({"question": query, "chat_history": history})
+    history.append((query, result["answer"]))
+    return result["answer"]
+
+def cache_checker(question, question_cache, chain):
+    # Check if the response is already cached
+    logging.info("I'm here")
+    if question in question_cache:
+        response = question_cache[question]
+        logging.info("Response retrieved from cache.")
+    else:
+        # Perform the Q&A operation
+        response = chain({"question": question})
+        question_cache[question] = response["answer"]
+        logging.info("Response computed and cached.")
+    
+    return response["answer"]
+
+def display_chat_history(chain):
+    reply_container = st.container()
+    container = st.container()
+    question_cache = {}
+    with container:
+        with st.form(key='my_form', clear_on_submit=True):
+            user_input = st.text_input("Question:", placeholder="Ask about your PDF", key='input')
+            submit_button = st.form_submit_button(label='Send')
+
+        if submit_button and user_input:
+            with st.spinner('Generating response...'):
+                output = conversation_chat(user_input, chain, st.session_state['history'])
+                # Check if the question is being cached
+                if user_input:
+                    if user_input in question_cache:
+                        st.info("Response retrieved from cache.")
+                        response = question_cache[user_input]
+                    else:
+                        st.info("Response computed.")
+                        response = cache_checker(user_input, question_cache, chain)
+                        question_cache[user_input] = response
+
+                    # Display the response
+                    st.write("Response:", response)
+
+            st.session_state['past'].append(user_input)
+            st.session_state['generated'].append(output)
+
+    if st.session_state['generated']:
+        with reply_container:
+            for i in range(len(st.session_state['generated'])):
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
+                message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
+
+def create_conversational_chain(vector_store):
+    # Create llm
+    llm = LlamaCpp(
+        streaming=True,
+        model_path="mistral-7b-instruct-v0.1.Q2_K.gguf",
+        temperature=0.75,
+        top_p=1, 
+        verbose=True,
+        n_ctx=4096
+    )
+    # llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta",  model_kwargs={
+    #     "temperature": 0.75,
+    #     "n_ctx": 4096,
+    #     "streaming":True,
+    #     "top_p": 0.99,
+    #     "verbose": True,
+    #     "max_length": 4096
+    # })
+
+    
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+    chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
+                                                retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
+                                                memory=memory)
+    return chain
+
+def main():
+    # Initialize session state
+    initialize_session_state()
+    st.title("Medbot :books:")
+    # Initialize Streamlit
+    st.sidebar.title("Document Processing")
+    uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True)
+
+    if uploaded_files:
+        text = []
+        for file in uploaded_files:
+            file_extension = os.path.splitext(file.name)[1]
+            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+                temp_file.write(file.read())
+                temp_file_path = temp_file.name
+       #  Initialize cache store
+            cache_store = LocalFileStore("./cache/")
+            loader = None
+            if file_extension == ".pdf":
+                loader = PyPDFLoader(temp_file_path)
+
+            if loader:
+                text.extend(loader.load())
+                os.remove(temp_file_path)
+
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        text_chunks = text_splitter.split_documents(text)
+
+       # Create embeddings
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", 
+                                           model_kwargs={'device': 'cpu'})
+
+        # Create cache-backed embeddings
+        cached_embeddings = CacheBackedEmbeddings.from_bytes_store(embeddings, cache_store, namespace="embeddings")
+
+         # Cache the embeddings
+        #cache_store.save("embeddings", cached_embeddings)
+
+        # Create vector store
+        vector_store = FAISS.from_documents(text_chunks, embedding=cached_embeddings)
+
+
+        # Create the chain object
+        chain = create_conversational_chain(vector_store)
+
+        display_chat_history(chain)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,13 @@
+langchain
+torch
+accelerate
+sentence_transformers
+streamlit_chat
+streamlit
+faiss-cpu
+tiktoken
+huggingface-hub
+pypdf
+llama-cpp-python
+
+