app.py

import os
from langchain.document_loaders import TextLoader, DirectoryLoader
from langchain.vectorstores import FAISS
from sentence_transformers import SentenceTransformer
import faiss
import torch
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
from datetime import datetime
import gradio as gr

class DocumentRetrievalAndGeneration:
    def __init__(self, embedding_model_name, lm_model_id, data_folder, faiss_index_path):
        self.documents = self.load_documents(data_folder)
        self.embeddings = SentenceTransformer(embedding_model_name)
        self.gpu_index = self.load_faiss_index(faiss_index_path)
        self.llm = self.initialize_llm(lm_model_id)

    def load_documents(self, folder_path):
        loader = DirectoryLoader(folder_path, loader_cls=TextLoader)
        documents = loader.load()
        print('Length of documents:', len(documents))
        return documents

    def load_faiss_index(self, faiss_index_path):
        cpu_index = faiss.read_index(faiss_index_path)
        gpu_resource = faiss.StandardGpuResources()
        gpu_index = faiss.index_cpu_to_gpu(gpu_resource, 0, cpu_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
        )
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        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=2048,
        )
        return generate_text

    def query_and_generate_response(self, query):
        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.documents[idx].page_content + "\n"
            print(self.documents[idx].page_content)
            print("############################")
        
        prompt = f"Query: {query}\nSolution: {content}\n"

        # Encode and prepare inputs
        messages = [{"role": "user", "content": prompt}]
        encodeds = self.llm.tokenizer.apply_chat_template(messages, return_tensors="pt")
        model_inputs = encodeds.to(self.llm.device)

        # Perform inference and measure time
        start_time = datetime.now()
        generated_ids = self.llm.model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
        elapsed_time = datetime.now() - start_time

        # Decode and return output
        decoded = self.llm.tokenizer.batch_decode(generated_ids)
        generated_response = decoded[0]
        print("Generated response:", generated_response)
        print("Time elapsed:", elapsed_time)
        print("Device in use:", self.llm.device)

        return generated_response

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

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_folder'
    faiss_index_path = 'faiss_index_new_model3.index'

    doc_retrieval_gen = DocumentRetrievalAndGeneration(embedding_model_name, lm_model_id, data_folder, faiss_index_path)

    # 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 = ["TDA4 product planning and datasheet release progress? ", 
                    "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?", 
                    "Master core in TDA2XX is a15 and in TDA3XX it is m4,so we have to shift all modules that are being used by a15 in TDA2XX to m4 in TDA3xx."]
        
        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"),
            css=css_code
        )

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

    # Launch the interface
    launch_interface()