app.py

import torch
import json
from torch import cuda, bfloat16
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig, StoppingCriteria, StoppingCriteriaList
from langchain.llms import HuggingFacePipeline
import gradio as gr
import os
import faiss
import numpy as np
from langchain.embeddings import HuggingFaceEmbeddings

class Chatbot:
    def __init__(self):
        self.HF_TOKEN = os.environ.get("HF_TOKEN", None)
        self.model_id = "mistralai/Mistral-7B-Instruct-v0.2"
        self.device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
        self.bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type='nf4',
            bnb_4bit_use_double_quant=True,
            bnb_4bit_compute_dtype=bfloat16
        )
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, token=self.HF_TOKEN)
        self.model = AutoModelForCausalLM.from_pretrained(self.model_id, device_map="auto", token=self.HF_TOKEN, quantization_config=self.bnb_config)
        self.stop_list = ['\nHuman:', '\n```\n']
        self.stop_token_ids = [self.tokenizer(x)['input_ids'] for x in self.stop_list]
        self.stop_token_ids = [torch.LongTensor(x).to(self.device) for x in self.stop_token_ids]
        self.stopping_criteria = StoppingCriteriaList([self.StopOnTokens()])

        self.generate_text = pipeline(
            model=self.model,
            tokenizer=self.tokenizer,
            return_full_text=True,
            task='text-generation',
            temperature=0.1,
            max_new_tokens=2048,
        )
        self.llm = HuggingFacePipeline(pipeline=self.generate_text)

        # Initialize the embedding model
        self.embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cuda"})

        try:
            cpu_index = faiss.read_index('faiss_index_new_model3.index')
            gpu_resource = faiss.StandardGpuResources()
            self.vectorstore = faiss.index_cpu_to_gpu(gpu_resource, 0, cpu_index)
            print("Loaded embedding successfully")
        except Exception as e:
            print("FAISS could not be imported or index could not be loaded.")
            raise e

        self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.vectorstore.as_retriever(), return_source_documents=True)
        self.chat_history = []

    class StopOnTokens(StoppingCriteria):
        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
            for stop_ids in self.stop_token_ids:
                if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
                    return True
            return False

    def format_prompt(self, query):
        prompt=f"""
        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 four relatable json files , 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.
        
        Here's my question:
        Query:{query}
        Solution==>
        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.",
        """
        return prompt

    def qa_infer(self, query):
        content = ""
        formatted_prompt = self.format_prompt(query)
        
        # Embed the query
        query_embedding = self.embeddings.embed_query(formatted_prompt)
        
        # Perform the search
        distances, indices = self.vectorstore.search(np.array([query_embedding]), k=5)
        
        # Retrieve the top documents
        for idx in indices[0]:
            doc = self.vectorstore.get_document(idx)
            content += "-" * 50 + "\n"
            content += doc.page_content + "\n"

        result = self.chain({"question": formatted_prompt, "chat_history": self.chat_history})
        print(content)
        print("#" * 100)
        print(result['answer'])

        output_file = "output.txt"
        with open(output_file, "w") as f:
            f.write("Query:\n")
            f.write(query + "\n\n")
            f.write("Answer:\n")
            f.write(result['answer'] + "\n\n")
            f.write("Source Documents:\n")
            f.write(content + "\n")

        download_link = f'<a href="file/{output_file}" download>Download Output File</a>'
        return result['answer'], content, download_link

    def launch_interface(self):
        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)
        
        tab1 = gr.Interface(fn=self.qa_infer, 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"), gr.HTML()], css=css_code)
        tab2 = gr.Interface(fn=self.qa_infer, inputs=[dropdown], allow_flagging='never', outputs=[gr.Textbox(label="SOLUTION"), gr.Textbox(label="RELATED QUERIES"), gr.HTML()], css=css_code)#, title="FAQs") 


        # # Add dummy outputs to each interface
        # tab1.outputs = dummy_outputs
        # tab2.outputs = dummy_outputs
        
        gr.TabbedInterface([tab1, tab2],["Textbox Input", "FAQs"],title="TI E2E FORUM",css=css_code).launch(debug=True)

# Instantiate and launch the chatbot
chatbot = Chatbot()
chatbot.launch_interface()