Update app.py

app.py

@@ -1,40 +1,57 @@
 import gradio as gr
 import os
+
 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 HuggingFaceEndpoint
+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
+# from chromadb.utils import get_default_config
+vector_db = ''
 
-# List of available LLM models
-list_llm = [
-    "mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-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"
+# 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]
 
+# 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()
     new_client = chromadb.EphemeralClient()
@@ -42,18 +59,102 @@ def create_db(splits, collection_name):
         documents=splits,
         embedding=embedding,
         client=new_client,
-        collection_name=collection_name
+        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})
+    
+    # HuggingFaceHub uses HF inference endpoints
     progress(0.5, desc="Initializing HF Hub...")
-    llm = HuggingFaceEndpoint(
-        repo_id=llm_model,
-        temperature=temperature,
-        max_new_tokens=max_tokens,
-        top_k=top_k
-    )
+    # 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,
+        )
     
     progress(0.75, desc="Defining buffer memory...")
     memory = ConversationBufferMemory(
@@ -61,50 +162,77 @@ 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_kwargs={'k': 5})  # Increased from 3 to 5
+    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
+    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
-    collection_name = collection_name.replace(" ", "-")
+    # 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]
-    progress(0.1, desc="Creating collection...")
+    print(list_file_path)
+    # Create collection_name for vector database
+    progress(0.1, desc="Creazione collezione...")
     collection_name = create_collection_name(list_file_path[0])
-    progress(0.25, desc="Loading documents...")
+    progress(0.25, desc="Caricamento documenti..")
+    # Load document and create splits
     doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
     
-    progress(0.5, desc="Generating vector database...")
+    # Creare o caricare il nuovo database
+    progress(0.5, desc="Generazione vector database...")
     vector_db = create_db(doc_splits, collection_name)
-    progress(0.9, desc="Done!")
+    progress(0.9, desc="Fatto!")
 
-    return vector_db, collection_name, "Completed!"
+    return vector_db, collection_name, "Completato!"
 
 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(f"Nome del modello: {llm_name}")
+    
     qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
-    return qa_chain, "Completed!"
+    
+    return qa_chain, "Completato!"
 
 def format_chat_history(message, chat_history):
     formatted_chat_history = []
@@ -112,28 +240,34 @@ def format_chat_history(message, 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)
     
-    source_info = []
-    for i in range(min(5, len(response_sources))):  # Increased from 3 to 5
-        source = response_sources[i]
-        source_info.append({
-            'content': source.page_content.strip(),
-            'page': source.metadata["page"] + 1
-        })
-    
+    # Append user message and response to chat history
     new_history = history + [(message, response_answer)]
-    return qa_chain, gr.update(value=""), new_history, *[info['content'] for info in source_info], *[info['page'] for info in source_info]
+    # 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
+    
 
-# The rest of the code (demo function and UI setup) remains largely the same,
-# but update the outputs of the conversation function to handle 5 sources instead of 3.
 def upload_file(file_obj):
     list_file_path = []
     for idx, file in enumerate(file_obj):
@@ -196,6 +330,7 @@ def demo():
             with gr.Row():
                 qachain_btn = gr.Button("Inizializza Question Answering chain")
         
+        
         with gr.Tab("Passo 4 - Chatbot"):
             chatbot = gr.Chatbot(height=300)
             with gr.Accordion("Opzioni avanzate - Riferimenti ai documenti", open=False):
@@ -208,12 +343,6 @@ def demo():
                 with gr.Row():
                     doc_source3 = gr.Textbox(label="Riferimento 3", lines=2, container=True, scale=20)
                     source3_page = gr.Number(label="Pagina", scale=1)
-                with gr.Row():
-                    doc_source4 = gr.Textbox(label="Riferimento 4", lines=2, container=True, scale=20)
-                    source4_page = gr.Number(label="Pagina", scale=1)
-                with gr.Row():
-                    doc_source5 = gr.Textbox(label="Riferimento 5", lines=2, container=True, scale=20)
-                    source5_page = gr.Number(label="Pagina", scale=1)
             with gr.Row():
                 msg = gr.Textbox(placeholder="Inserisci messaggio (es. 'Di cosa tratta questo documento?')", container=True)
             with gr.Row():
@@ -225,49 +354,28 @@ def demo():
         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], \
-            outputs=[qa_chain, llm_progress]).then(lambda:[None, "", 0, "", 0, "", 0, "", 0, "", 0], \
+            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
             inputs=None, \
-            outputs=[chatbot, 
-                     doc_source1, source1_page, 
-                     doc_source2, source2_page, 
-                     doc_source3, source3_page,
-                     doc_source4, source4_page,
-                     doc_source5, source5_page], queue=False)
+            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
+            queue=False)
 
         # Chatbot events
         msg.submit(conversation, \
             inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, \
-                     doc_source1, source1_page, 
-                     doc_source2, source2_page, 
-                     doc_source3, source3_page,
-                     doc_source4, source4_page,
-                     doc_source5, source5_page], queue=False)
-        submit_btn.click(conversation, 
-            inputs=[qa_chain, msg, chatbot], 
-            outputs=[qa_chain, msg, chatbot, 
-                     doc_source1, source1_page, 
-                     doc_source2, source2_page, 
-                     doc_source3, source3_page,
-                     doc_source4, source4_page,
-                     doc_source5, source5_page], queue=False)
-        clear_btn.click(
-            lambda: [None, "", 0, "", 0, "", 0, "", 0, "", 0],
-            inputs=None,
-            outputs=[
-                chatbot,
-                doc_source1, source1_page,
-                doc_source2, source2_page,
-                doc_source3, source3_page,
-                doc_source4, source4_page,
-                doc_source5, source5_page
-            ],
-            queue=False
-        )
+            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
+            queue=False)
+        submit_btn.click(conversation, \
+            inputs=[qa_chain, msg, chatbot], \
+            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
+            queue=False)
+        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
+            inputs=None, \
+            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()