app.py

import concurrent.futures
import threading
import torch
from datetime import datetime
import json
import gradio as gr
import re
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
from langchain.document_loaders import DirectoryLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

class DocumentRetrievalAndGeneration:
    def __init__(self, embedding_model_name, lm_model_id, data_folder):
        self.all_splits = self.load_documents(data_folder)
        self.embeddings = SentenceTransformer(embedding_model_name)
        self.gpu_index = self.create_faiss_index()
        self.llm = self.initialize_llm(lm_model_id)
        self.cancel_flag = threading.Event()

    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(5):
            print(all_splits[i].page_content)
        return all_splits

    def create_faiss_index(self):
        all_texts = [split.page_content for split in self.all_splits]
        embeddings = self.embeddings.encode(all_texts, convert_to_tensor=True).cpu().numpy()
        index = faiss.IndexFlatL2(embeddings.shape[1])
        index.add(embeddings)
        gpu_resource = faiss.StandardGpuResources()
        gpu_index = faiss.index_cpu_to_gpu(gpu_resource, 0, index)
        return gpu_index

    def initialize_llm(self, model_id):
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.bfloat16
        )
        model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        generate_text = pipeline(
            model=model,
            tokenizer=tokenizer,
            return_full_text=True,
            task='text-generation',
            temperature=0.6,
            max_new_tokens=256,
        )
        return generate_text

    def generate_response_with_timeout(self, model_inputs):
        def target(future):
            if self.cancel_flag.is_set():
                return
            generated_ids = self.llm.model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
            if not self.cancel_flag.is_set():
                future.set_result(generated_ids)
            else:
                future.set_exception(TimeoutError("Text generation process was canceled"))

        future = concurrent.futures.Future()
        thread = threading.Thread(target=target, args=(future,))
        thread.start()

        try:
            generated_ids = future.result(timeout=60)  # Timeout set to 60 seconds
            return generated_ids
        except concurrent.futures.TimeoutError:
            self.cancel_flag.set()
            raise TimeoutError("Text generation process timed out")

    def qa_infer_gradio(self, query):
        # Set the cancel flag to false for the new query
        self.cancel_flag.clear()

        try:
            query_embedding = self.embeddings.encode(query, convert_to_tensor=True).cpu().numpy()
            distances, indices = self.gpu_index.search(np.array([query_embedding]), k=5)

            content = ""
            for idx in indices[0]:
                content += "-" * 50 + "\n"
                content += self.all_splits[idx].page_content + "\n"

            prompt = f"""<s>
            You are a knowledgeable assistant with access to a comprehensive database. 
            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
            Here's what I need:
            Include a final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point.
            content
            Here's my question:
            Query:{query}
            Solution==>
            RETURN ONLY SOLUTION . IF THEIR IS NO ANSWER RELATABLE IN RETRIEVED CHUNKS , RETURN " NO SOLUTION AVAILABLE"
            Example1
            Query: "How to use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM",
            Solution: "To use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM, you need to modify the configuration file of the NDK application. Specifically, change the processor reference from 'A15_0' to 'IPU1_0'.",
            
            Example2
            Query: "Can BQ25896 support I2C interface?",
            Solution: "Yes, the BQ25896 charger supports the I2C interface for communication."
            </s>
            """
            
            messages = [{"role": "user", "content": prompt}]
            encodeds = self.llm.tokenizer.apply_chat_template(messages, return_tensors="pt")
            model_inputs = encodeds.to(self.llm.model.device)

            start_time = datetime.now()
            generated_ids = self.generate_response_with_timeout(model_inputs)
            elapsed_time = datetime.now() - start_time

            decoded = self.llm.tokenizer.batch_decode(generated_ids)
            generated_response = decoded[0]

            match = re.search(r'Solution:(.*?)</s>', generated_response, re.DOTALL | re.IGNORECASE)
            if match:
                solution_text = match.group(1).strip()
            else:
                solution_text = "NO SOLUTION AVAILABLE"

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

            return solution_text, content

        except TimeoutError:
            return "timeout", content

if __name__ == "__main__":
    # Example usage
    embedding_model_name = 'flax-sentence-embeddings/all_datasets_v3_MiniLM-L12'
    lm_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
    data_folder = 'sample_embedding_folder2'

    doc_retrieval_gen = DocumentRetrievalAndGeneration(embedding_model_name, lm_model_id, data_folder)

    # Define Gradio interface function
    def launch_interface():
        css_code = """
            .gradio-container {
                background-color: #daccdb;
            }
            /* Button styling for all buttons */
            button {
                background-color: #927fc7; /* Default color for all other buttons */
                color: black;
                border: 1px solid black;
                padding: 10px;
                margin-right: 10px;
                font-size: 16px; /* Increase font size */
                font-weight: bold; /* Make text bold */
            }
            """
        EXAMPLES = ["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?"]
        
        file_path = "ticketNames.txt"

        # Read the file content
        with open(file_path, "r") as file:
            content = file.read()
        ticket_names = json.loads(content)
        dropdown = gr.Dropdown(label="Sample queries", choices=ticket_names)
        
        # Define Gradio interface
        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="SOLUTION"), gr.Textbox(label="RELATED QUERIES")],
            css=css_code
        )

        # Launch Gradio interface
        interface.launch(debug=True)

    # Launch the interface
    launch_interface()