app.py

import os
import multiprocessing
import concurrent.futures
# from langchain.document_loaders import TextLoader, DirectoryLoader
from langchain_community.document_loaders import TextLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from transformers import AutoModel, AutoTokenizer
import torch.nn.functional as F
import faiss
import torch
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
from datetime import datetime
import json
import gradio as gr
import re
from threading import Thread
import os

class DocumentRetrievalAndGeneration:
    def __init__(self, embedding_model_name, lm_model_id, data_folder):
        self.all_splits = self.load_documents(data_folder)
        hf_token = os.getenv('HF_TOKEN')
        self.embedding_tokenizer = AutoTokenizer.from_pretrained(embedding_model_name, token=hf_token)
        self.embedding_model = AutoModel.from_pretrained(embedding_model_name, token=hf_token)
        self.gpu_index = self.create_faiss_index()
        self.tokenizer, self.model = self.initialize_llm(lm_model_id)

    def load_documents(self, folder_path):
        loader = DirectoryLoader(folder_path, loader_cls=TextLoader)
        documents = loader.load()
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=250)
        all_splits = text_splitter.split_documents(documents)
        print('Length of documents:', len(documents))
        print("LEN of all_splits", len(all_splits))
        for i in range(min(3, len(all_splits))):
            print(all_splits[i].page_content)
        return all_splits

    def encode_texts(self, texts):
        encoded_input = self.embedding_tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors='pt')
        with torch.no_grad():
            model_output = self.embedding_model(**encoded_input)
            embeddings = self.mean_pooling(model_output, encoded_input['attention_mask'])
            embeddings = F.normalize(embeddings, p=2, dim=1)
        return embeddings.cpu().numpy()

    def mean_pooling(self, model_output, attention_mask):
        token_embeddings = model_output[0]
        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

    def create_faiss_index(self):
        all_texts = [split.page_content for split in self.all_splits]

        batch_size = 256
        all_embeddings = []

        for i in range(0, len(all_texts), batch_size):
            batch_texts = all_texts[i:i+batch_size]
            batch_embeddings = self.encode_texts(batch_texts)
            all_embeddings.append(batch_embeddings)
            print(f"Processed batch {i//batch_size + 1}/{(len(all_texts) + batch_size - 1)//batch_size}")

        embeddings = np.vstack(all_embeddings)
        index = faiss.IndexFlatL2(embeddings.shape[1])
        index.add(embeddings)

        try:
            if torch.cuda.is_available():
                gpu_resource = faiss.StandardGpuResources()
                gpu_index = faiss.index_cpu_to_gpu(gpu_resource, 0, index)
                print("Using GPU for FAISS")
                return gpu_index
            else:
                print("Using CPU for FAISS")
                return index
        except Exception as e:
            print(f"GPU FAISS failed: {e}, using CPU")
            return index

    def initialize_llm(self, model_id):
        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.bfloat16
        )
        hf_token = "replace_your_token_here"
        print(f"Token found: {hf_token is not None}")
        print(f"LLM Token found: {hf_token is not None}")
        print(f"Token starts with: {hf_token[:10] if hf_token else 'None'}...")
        tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)

        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.bfloat16,
            device_map="auto",
            quantization_config=quantization_config,
            token=hf_token
        )
        return tokenizer, model

    def generate_response_with_timeout(self, input_ids, max_new_tokens=1000):
        try:
            streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
            generate_kwargs = dict(
                input_ids=input_ids,
                max_new_tokens=max_new_tokens,
                do_sample=True,
                top_p=1.0,
                top_k=20,
                temperature=0.8,
                repetition_penalty=1.2,
                pad_token_id=self.tokenizer.eos_token_id,  
                eos_token_id=self.tokenizer.eos_token_id,  
                streamer=streamer,
            )

            thread = Thread(target=self.model.generate, kwargs=generate_kwargs)
            thread.start()

            generated_text = ""
            for new_text in streamer:
                generated_text += new_text

            thread.join()  
            return generated_text
        except Exception as e:
            print(f"Error in generate_response_with_timeout: {str(e)}")
            return "Text generation process encountered an error"

    def query_and_generate_response(self, query):
        similarityThreshold = 1
        query_embedding = self.encode_texts([query])[0]
        distances, indices = self.gpu_index.search(np.array([query_embedding]), k=3)
        print("Distance", distances, "indices", indices)
        content = ""
        filtered_results = []
        for idx, distance in zip(indices[0], distances[0]):
            if distance <= similarityThreshold:
                filtered_results.append(idx)
            for i in filtered_results:
                print(self.all_splits[i].page_content)
            content += "-" * 50 + "\n"
            content += self.all_splits[idx].page_content + "\n"
            print("CHUNK", idx)
            print("Distance:", distance)
            print("indices:", indices)
            print(self.all_splits[idx].page_content)
            print("############################")

        conversation = [
            {"role": "system", "content": "You are a knowledgeable assistant with access to a comprehensive database."},
            {"role": "user", "content": f"""
            I need you to answer my question and provide related information in a specific format.
            I have provided five relatable json files {content}, choose the most suitable chunks for answering the query.
            RETURN ONLY SOLUTION without additional comments, sign-offs, retrived chunks, refrence to any Ticket or extra phrases. Be direct and to the point.
            IF THERE IS NO ANSWER RELATABLE IN RETRIEVED CHUNKS, RETURN "NO SOLUTION AVAILABLE".
            DO NOT GIVE REFRENCE TO ANY CHUNKS OR TICKETS,BE ON POINT.

            Here's my question:
            Query: {query}
            Solution==>
            """}
        ]
       
        input_ids = self.tokenizer.apply_chat_template(conversation, return_tensors="pt").to(self.model.device)

        start_time = datetime.now()
        generated_response = self.generate_response_with_timeout(input_ids)
        elapsed_time = datetime.now() - start_time

        print("Generated response:", generated_response)
        print("Time elapsed:", elapsed_time)
        print("Device in use:", self.model.device)

        solution_text = generated_response.strip()
        if "Solution:" in solution_text:
            solution_text = solution_text.split("Solution:", 1)[1].strip()

        solution_text = re.sub(r'^assistant\s*', '', solution_text, flags=re.IGNORECASE)
        solution_text = solution_text.strip()

        return solution_text, content

    def qa_infer_gradio(self, query):
        response = self.query_and_generate_response(query)
        return response

if __name__ == "__main__":
    embedding_model_name = 'sentence-transformers/all-MiniLM-L6-v2'
    lm_model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
    data_folder = 'sample_embedding_folder2'

    doc_retrieval_gen = DocumentRetrievalAndGeneration(embedding_model_name, lm_model_id, data_folder)

    def launch_interface():
        css_code = """
            .gradio-container {
                background-color: #daccdb;
            }
            button {
                background-color: #927fc7;
                color: black;
                border: 1px solid black;
                padding: 10px;
                margin-right: 10px;
                font-size: 16px;
                font-weight: bold;
            }
        """
        EXAMPLES = [
            "On which devices can the VIP and CSI2 modules operate simultaneously?",
            "I'm using Code Composer Studio 5.4.0.00091 and enabled FPv4SPD16 floating point support for CortexM4 in TDA2. However, after building the project, the .asm file shows --float_support=vfplib instead of FPv4SPD16. Why is this happening?",
            "Could you clarify the maximum number of cameras that can be connected simultaneously to the video input ports on the TDA2x SoC, considering it supports up to 10 multiplexed input ports and includes 3 dedicated video input modules?"
        ]

        interface = gr.Interface(
            fn=doc_retrieval_gen.qa_infer_gradio,
            inputs=[gr.Textbox(label="QUERY", placeholder="Enter your query here")],
            allow_flagging='never',
            examples=EXAMPLES,
            cache_examples=False,
            outputs=[gr.Textbox(label="RESPONSE"), gr.Textbox(label="RELATED QUERIES")],
            css=css_code,
            title="TI E2E FORUM"
        )

        interface.launch(debug=True)

    launch_interface()