Update app.py

app.py

@@ -1,56 +1,50 @@
 import gradio as gr
 import os
-
+from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import Chroma
 from langchain.chains import ConversationalRetrievalChain
 from langchain_community.embeddings import HuggingFaceEmbeddings 
-from langchain_community.llms import HuggingFacePipeline
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
-from langchain_community.llms import HuggingFaceEndpoint
-
 from pathlib import Path
 import chromadb
 from unidecode import unidecode
-
-from transformers import AutoTokenizer
-import transformers
-import torch
-import tqdm 
-import accelerate
 import re
 
-
-
-# default_persist_directory = './chroma_HF/'
-list_llm = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1", \
-    "google/gemma-7b-it","google/gemma-2b-it", \
-    "HuggingFaceH4/zephyr-7b-beta", "HuggingFaceH4/zephyr-7b-gemma-v0.1", \
-    "meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2", \
-    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct", \
-    "google/flan-t5-xxl"
-]
-list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+# Define MuRIL model and tokenizer
+muril_tokenizer = AutoTokenizer.from_pretrained("google/muril-base-cased")
+muril_model = AutoModelForMaskedLM.from_pretrained("google/muril-base-cased")
+
+# Function to initialize MuRIL pipeline
+def initialize_muril_pipeline(temperature, max_tokens, top_k):
+    muril_pipeline = pipeline(
+        "text-generation",
+        model=muril_model,
+        tokenizer=muril_tokenizer,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        top_k=top_k,
+        num_return_sequences=1,
+        eos_token_id=muril_tokenizer.eos_token_id
+    )
+    return muril_pipeline
 
 # Load PDF document and create doc splits
 def load_doc(list_file_path, chunk_size, chunk_overlap):
-    # Processing for one document only
-    # loader = PyPDFLoader(file_path)
-    # pages = loader.load()
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
     for loader in loaders:
         pages.extend(loader.load())
-    # text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
     text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size = chunk_size, 
-        chunk_overlap = chunk_overlap)
+        chunk_size=chunk_size, 
+        chunk_overlap=chunk_overlap)
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
-
 # Create vector database
 def create_db(splits, collection_name):
     embedding = HuggingFaceEmbeddings()
@@ -60,102 +54,16 @@ def create_db(splits, collection_name):
         embedding=embedding,
         client=new_client,
         collection_name=collection_name,
-        # persist_directory=default_persist_directory
     )
     return vectordb
 
-
-# Load vector database
-def load_db():
-    embedding = HuggingFaceEmbeddings()
-    vectordb = Chroma(
-        # persist_directory=default_persist_directory, 
-        embedding_function=embedding)
-    return vectordb
-
-
-# Initialize langchain LLM chain
-def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    progress(0.1, desc="Initializing HF tokenizer...")
-    # HuggingFacePipeline uses local model
-    # Note: it will download model locally...
-    # tokenizer=AutoTokenizer.from_pretrained(llm_model)
-    # progress(0.5, desc="Initializing HF pipeline...")
-    # pipeline=transformers.pipeline(
-    #     "text-generation",
-    #     model=llm_model,
-    #     tokenizer=tokenizer,
-    #     torch_dtype=torch.bfloat16,
-    #     trust_remote_code=True,
-    #     device_map="auto",
-    #     # max_length=1024,
-    #     max_new_tokens=max_tokens,
-    #     do_sample=True,
-    #     top_k=top_k,
-    #     num_return_sequences=1,
-    #     eos_token_id=tokenizer.eos_token_id
-    #     )
-    # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
+# Initialize langchain LLM chain using MuRIL
+def initialize_llmchain(temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    progress(0.1, desc="Initializing MuRIL model...")
+    muril_pipeline = initialize_muril_pipeline(temperature, max_tokens, top_k)
     
-    # HuggingFaceHub uses HF inference endpoints
-    progress(0.5, desc="Initializing HF Hub...")
-    # Use of trust_remote_code as model_kwargs
-    # Warning: langchain issue
-    # URL: https://github.com/langchain-ai/langchain/issues/6080
-    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-            load_in_8bit = True,
-        )
-    elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]:
-        raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint")
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
-    elif llm_model == "microsoft/phi-2":
-        # raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-            trust_remote_code = True,
-            torch_dtype = "auto",
-        )
-    elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
-            temperature = temperature,
-            max_new_tokens = 250,
-            top_k = top_k,
-        )
-    elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
-        raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
-    else:
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
+    # Integrate pipeline with langchain
+    llm = HuggingFacePipeline(pipeline=muril_pipeline, model_kwargs={'temperature': temperature})
     
     progress(0.75, desc="Defining buffer memory...")
     memory = ConversationBufferMemory(
@@ -163,123 +71,25 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
         output_key='answer',
         return_messages=True
     )
-    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
-    retriever=vector_db.as_retriever()
+    retriever = vector_db.as_retriever()
     progress(0.8, desc="Defining retrieval chain...")
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         retriever=retriever,
         chain_type="stuff", 
         memory=memory,
-        # combine_docs_chain_kwargs={"prompt": your_prompt})
         return_source_documents=True,
-        #return_generated_question=False,
         verbose=False,
     )
     progress(0.9, desc="Done!")
     return qa_chain
 
-
-# Generate collection name for vector database
-#  - Use filepath as input, ensuring unicode text
-def create_collection_name(filepath):
-    # Extract filename without extension
-    collection_name = Path(filepath).stem
-    # Fix potential issues from naming convention
-    ## Remove space
-    collection_name = collection_name.replace(" ","-") 
-    ## ASCII transliterations of Unicode text
-    collection_name = unidecode(collection_name)
-    ## Remove special characters
-    #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
-    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-    ## Limit length to 50 characters
-    collection_name = collection_name[:50]
-    ## Minimum length of 3 characters
-    if len(collection_name) < 3:
-        collection_name = collection_name + 'xyz'
-    ## Enforce start and end as alphanumeric character
-    if not collection_name[0].isalnum():
-        collection_name = 'A' + collection_name[1:]
-    if not collection_name[-1].isalnum():
-        collection_name = collection_name[:-1] + 'Z'
-    print('Filepath: ', filepath)
-    print('Collection name: ', collection_name)
-    return collection_name
-
-
-# Initialize database
-def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
-    # Create list of documents (when valid)
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    # Create collection_name for vector database
-    progress(0.1, desc="Creating collection name...")
-    collection_name = create_collection_name(list_file_path[0])
-    progress(0.25, desc="Loading document...")
-    # Load document and create splits
-    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
-    # Create or load vector database
-    progress(0.5, desc="Generating vector database...")
-    # global vector_db
-    vector_db = create_db(doc_splits, collection_name)
-    progress(0.9, desc="Done!")
-    return vector_db, collection_name, "Complete!"
-
-
-def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    # print("llm_option",llm_option)
-    llm_name = list_llm[llm_option]
-    print("llm_name: ",llm_name)
-    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
+# Initialize the LLM chain for your chatbot
+def initialize_LLM(llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    qa_chain = initialize_llmchain(llm_temperature, max_tokens, top_k, vector_db, progress)
     return qa_chain, "Complete!"
 
-
-def format_chat_history(message, chat_history):
-    formatted_chat_history = []
-    for user_message, bot_message in chat_history:
-        formatted_chat_history.append(f"User: {user_message}")
-        formatted_chat_history.append(f"Assistant: {bot_message}")
-    return formatted_chat_history
-    
-
-def conversation(qa_chain, message, history):
-    formatted_chat_history = format_chat_history(message, history)
-    #print("formatted_chat_history",formatted_chat_history)
-   
-    # Generate response using QA chain
-    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
-    response_answer = response["answer"]
-    if response_answer.find("Helpful Answer:") != -1:
-        response_answer = response_answer.split("Helpful Answer:")[-1]
-    response_sources = response["source_documents"]
-    response_source1 = response_sources[0].page_content.strip()
-    response_source2 = response_sources[1].page_content.strip()
-    response_source3 = response_sources[2].page_content.strip()
-    # Langchain sources are zero-based
-    response_source1_page = response_sources[0].metadata["page"] + 1
-    response_source2_page = response_sources[1].metadata["page"] + 1
-    response_source3_page = response_sources[2].metadata["page"] + 1
-    # print ('chat response: ', response_answer)
-    # print('DB source', response_sources)
-    
-    # Append user message and response to chat history
-    new_history = history + [(message, response_answer)]
-    # return gr.update(value=""), new_history, response_sources[0], response_sources[1] 
-    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
-    
-
-def upload_file(file_obj):
-    list_file_path = []
-    for idx, file in enumerate(file_obj):
-        file_path = file_obj.name
-        list_file_path.append(file_path)
-    # print(file_path)
-    # initialize_database(file_path, progress)
-    return list_file_path
-
-
-
-
+# Demo function with Gradio UI
 def demo():
     with gr.Blocks(theme="base") as demo:
         vector_db = gr.State()
@@ -309,23 +119,18 @@ def demo():
 
         with gr.Tab("Step 3 - Initialize QA chain"):
             with gr.Row():
-                llm_btn = gr.Radio(list_llm_simple, \
-                    label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
-            with gr.Accordion("Advanced options - LLM model", open=False):
-                with gr.Row():
-                    slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
-                with gr.Row():
-                    slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
-                with gr.Row():
-                    slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
+                with gr.Accordion("Advanced options - LLM model", open=False):
+                    with gr.Row():
+                        slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
+                    with gr.Row():
+                        slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                    with gr.Row():
+                        slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
             with gr.Row():
-                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
+                llm_progress = gr.Textbox(value="None", label="QA chain initialization")
             with gr.Row():
                 qachain_btn = gr.Button("Initialize Question Answering chain")
 
-   
-
-
         with gr.Tab("Step 4: Chatbot"):
             chatbot = gr.Chatbot(label="Chat with your PDF", height=300)
             with gr.Accordion("Advanced: Document References", open=False):
@@ -347,7 +152,7 @@ def demo():
         generate_db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress])
         qachain_btn.click(
             initialize_LLM, 
-            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], 
+            inputs=[slider_temperature, slider_maxtokens, slider_topk, vector_db], 
             outputs=[qa_chain, llm_progress]
         ).then(
             lambda: [None, "", 0, "", 0, "", 0],
@@ -375,9 +180,5 @@ def demo():
             outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
             queue=False
         )
-    demo.queue().launch(debug=True)
-
-
-if __name__ == "__main__":
-    demo()
 
+    demo.launch()