Update app.py

app.py

@@ -1,64 +1,235 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import time
+import os
+import glob
+import textwrap
+import torch
+from transformers import (
+    AutoTokenizer, AutoModelForCausalLM,
+    BitsAndBytesConfig,
+    pipeline
+)
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.llms import HuggingFacePipeline
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+
+# Configuration class
+class CFG:
+    # LLMs
+    model_name = 'llama2-13b-chat'  # wizardlm, llama2-7b-chat, llama2-13b-chat, mistral-7B
+    temperature = 0
+    top_p = 0.95
+    repetition_penalty = 1.15
+
+    # splitting
+    split_chunk_size = 800
+    split_overlap = 0
+
+    # embeddings
+    embeddings_model_repo = 'sentence-transformers/all-MiniLM-L6-v2'
+
+    # similar passages
+    k = 6
+
+    # paths
+    PDFs_path = './'  # Set to your PDF path
+    Embeddings_path = './faiss-hp-sentence-transformers'
+    Output_folder = './rag-vectordb'
+
+# Set preferred encoding to UTF-8 (for non-ASCII characters)
+import locale
+locale.getpreferredencoding = lambda: "UTF-8"
+
+# Function to get model
+def get_model(model = CFG.model_name):
+    print('\nDownloading model: ', model, '\n\n')
+    
+    if model == 'wizardlm':
+        model_repo = 'TheBloke/wizardLM-7B-HF'
+
+        tokenizer = AutoTokenizer.from_pretrained(model_repo)
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+        )
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_repo,
+            quantization_config=bnb_config,
+            device_map='auto',
+            low_cpu_mem_usage=True
+        )
+
+        max_len = 1024
+
+    elif model == 'llama2-7b-chat':
+        model_repo = 'daryl149/llama-2-7b-chat-hf'
+        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
+
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+        )
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_repo,
+            quantization_config=bnb_config,
+            device_map='auto',
+            low_cpu_mem_usage=True,
+            trust_remote_code=True
+        )
+
+        max_len = 2048
+
+    elif model == 'llama2-13b-chat':
+        model_repo = 'daryl149/llama-2-13b-chat-hf'
+        tokenizer = AutoTokenizer.from_pretrained(model_repo, use_fast=True)
+
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+        )
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_repo,
+            quantization_config=bnb_config,
+            low_cpu_mem_usage=True,
+            trust_remote_code=True
+        )
+
+        max_len = 2048
+
+    else:
+        print("Model not implemented!")
+
+    return tokenizer, model, max_len
+
+# Get the model
+tokenizer, model, max_len = get_model(CFG.model_name)
+
+# Set up Hugging Face pipeline
+pipe = pipeline(
+    task="text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    pad_token_id=tokenizer.eos_token_id,
+    max_length=max_len,
+    temperature=CFG.temperature,
+    top_p=CFG.top_p,
+    repetition_penalty=CFG.repetition_penalty
 )
 
+# Langchain pipeline
+llm = HuggingFacePipeline(pipeline=pipe)
+
+# Load the documents
+loader = DirectoryLoader(
+    CFG.PDFs_path,
+    glob="./*.pdf",
+    loader_cls=PyPDFLoader,
+    show_progress=True,
+    use_multithreading=True
+)
+documents = loader.load()
+
+# Split the documents
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=CFG.split_chunk_size,
+    chunk_overlap=CFG.split_overlap
+)
+texts = text_splitter.split_documents(documents)
+
+# Set up vector store
+vectordb = FAISS.from_documents(
+    texts,
+    HuggingFaceInstructEmbeddings(model_name=CFG.embeddings_model_repo)
+)
+
+# Save the vector store
+vectordb.save_local(f"{CFG.Output_folder}/faiss_index_rag")
+
+# Define the prompt template
+prompt_template = """
+Don't try to make up an answer, if you don't know just say that you don't know.
+Answer in the same language the question was asked.
+Use only the following pieces of context to answer the question at the end.
+
+{context}
+
+Question: {question}
+Answer:"""
+
+PROMPT = PromptTemplate(
+    template=prompt_template,
+    input_variables=["context", "question"]
+)
+
+# Set up retriever
+retriever = vectordb.as_retriever(search_kwargs={"k": CFG.k, "search_type": "similarity"})
+
+# Create the retrieval-based QA chain
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",  # other options: "map_reduce", "map_rerank", "refine"
+    retriever=retriever,
+    chain_type_kwargs={"prompt": PROMPT},
+    return_source_documents=True,
+    verbose=False
+)
+
+# Function to wrap text for proper display
+def wrap_text_preserve_newlines(text, width=700):
+    lines = text.split('\n')
+    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
+    wrapped_text = '\n'.join(wrapped_lines)
+    return wrapped_text
+
+# Function to process model response
+def process_llm_response(llm_response):
+    ans = wrap_text_preserve_newlines(llm_response['result'])
+    sources_used = ' \n'.join(
+        [
+            source.metadata['source'].split('/')[-1][:-4]
+            + ' - page: '
+            + str(source.metadata['page'])
+            for source in llm_response['source_documents']
+        ]
+    )
+    ans = ans + '\n\nSources: \n' + sources_used
+    return ans
+
+# Function to get the answer from the model
+def llm_ans(query):
+    start = time.time()
+    llm_response = qa_chain.invoke(query)
+    ans = process_llm_response(llm_response)
+    end = time.time()
+
+    time_elapsed = int(round(end - start, 0))
+    time_elapsed_str = f'\n\nTime elapsed: {time_elapsed} s'
+    return ans + time_elapsed_str
+
+# Function for Gradio chat interface
+def predict(message, history):
+    output = str(llm_ans(message)).replace("\n", "<br/>")
+    return output
+
+# Set up Gradio interface
+demo = gr.ChatInterface(
+    fn=predict,
+    title=f'Open-Source LLM ({CFG.model_name}) Question Answering'
+)
 
-if __name__ == "__main__":
-    demo.launch()
+# Start the Gradio interface
+demo.queue()
+demo.launch()