Delete app.py

app.py

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-# -*- coding: utf-8 -*-
-"""Mech_bot_monitor_eval.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1_Y_wngb4w6axUfB6jOwRv_DR_DPzx_1M
-"""
-
-# Commented out IPython magic to ensure Python compatibility.
-# %pip install --upgrade langchain-together
-
-"""# 🔧🚗**Mech-Bot 🤖🔧: Automotive Mechanic Assistant**
-
-Welcome to Mech-Bot, your personal automotive mechanic assistant. Mech-Bot leverages RAG (Retrieval-Augmented Generation) and the capabilities of LangChain to provide comprehensive support for diagnosing and repairing vehicle issues. This project aims to make mechanical expertise accessible, reliable, and efficient, addressing common challenges faced by car owners and mechanics.
-
-
-
-
-😉**Key features include:**
-
-- AI Diagnostic Support.
-- Ease of dealing with Repair Manuals.
-- Real-Time Assistance
-
-
-
-📚🔧**Data to Use:**
-- Repair Manuals: Detailed, step-by-step instructions on car maintenance and repair, available in digital formats like PDFs, and loaded using PyMuPDF.
-
-**🦾Technology Stack for Mech-Bot:**
-
-### **part 1: Tools and Dependencies**
-"""
-
-
-
-# pip install -U bitsandbytes
-
-import os
-os.environ["WEAVE_TRACE_LANGCHAIN"] = "true"
-import weave
-import wandb
-
-wandb.Api(api_key='e27ce4c7835957d03d50e03f0bd8182ace127cbd')
-
-
-wandb.login(key= 'e27ce4c7835957d03d50e03f0bd8182ace127cbd')
-weave.init('Mech-Bot-Evaluation')
-
-"""### **part 2: Data Collection and Preprocessing**"""
-
-from langchain.document_loaders import PyMuPDFLoader
-all_documents = ""
-
-# Load PDF data
-pdf_loader = PyMuPDFLoader("Mech-bot_DATA(text).pdf")
-pdf_documents = pdf_loader.load()
-
-# Extract text from the PDF documents
-for pdf_doc in pdf_documents:
-    all_documents += pdf_doc.page_content
-
-""" ### **Part 3: split the text**"""
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-# Split documents
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=31)
-splits = text_splitter.split_text(all_documents)
-
-
-# Output the split content (for demonstration purposes)
-for i, split in enumerate(splits):
-    print(f"Split {i + 1}: {split[:100]}...")  # Print the first 100 characters of each split
-
-"""### **part 4: save embeddings in a vector store**"""
-
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.schema import Document
-
-# Convert split strings to Document objects
-documents = [Document(page_content=split) for split in splits]
-
-
-# Add source metadata to each document
-for doc in documents:
-    doc.metadata["source"] = "https://club.autodoc.co.uk/manuals"
-# Create the FAISS vector store
-db = FAISS.from_documents(documents,
-                          HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))
-
-"""### **part 5: create the retriever**"""
-
-retriever = db.as_retriever(
-    search_type="similarity",
-    search_kwargs={'k': 4}
-)
-
-print(documents)
-
-# Manually test retrieval to verify correct chunk fetching
-query = "what are the tools needed to change Brake Discs on a Car?"
-retrieved_docs = db.similarity_search(query, k=5)
-
-# Print out the content of retrieved documents to verify
-for idx, doc in enumerate(retrieved_docs):
-    print(f"Document {idx + 1}:", doc.page_content)
-
-"""### **part 6: load the llama model + Quantization**"""
-
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-from transformers import LlamaTokenizer, LlamaForCausalLM
-
-
-model_name = "meta-llama/Llama-2-13b-chat-hf"
-
-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_name,
-    quantization_config=bnb_config,
-    torch_dtype=torch.bfloat16,
-    # device_map="auto",
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-"""### **part 7:Load the Llama Guard model**"""
-
-model_id = "meta-llama/LlamaGuard-7b"
-guard_tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-bnb_config_guard = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16,
-    # Allow offloading to CPU for parts of the model
-    load_in_8bit_fp32_cpu_offload=True
-)
-guard_model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    quantization_config=bnb_config_guard,
-    torch_dtype=torch.bfloat16,
-    # device_map="auto",
-    )
-
-"""### **part 8: Setting Up a Text Generation Pipeline with HuggingFace and LangChain**"""
-
-from langchain.llms import HuggingFacePipeline
-from langchain.prompts import PromptTemplate
-from transformers import pipeline
-from langchain_core.output_parsers import StrOutputParser
-
-
-text_generation_pipeline = pipeline(
-    model=model,
-    tokenizer=tokenizer,
-    task="text-generation",
-    temperature=0.2,
-    do_sample=True,
-    repetition_penalty=1.1,
-    return_full_text=False,
-    max_new_tokens=512,
-    num_return_sequences=1,
-    eos_token_id=tokenizer.eos_token_id,
-)
-
-llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
-
-"""### **part 9: Creating a History-Aware Question Retriever with LangChain**"""
-
-from langchain.chains import create_history_aware_retriever
-from langchain.prompts.chat import MessagesPlaceholder
-from langchain.prompts import ChatPromptTemplate
-
-
-contextualize_q_system_prompt = (
-    "Given a chat history and the latest user question "
-    "which might reference context in the chat history, "
-    "formulate a standalone question which can be understood "
-    "without the chat history. Do NOT answer the question, "
-    "just reformulate it if needed and otherwise return it as is."
-)
-contextualize_q_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", contextualize_q_system_prompt),
-        MessagesPlaceholder("chat_history"),
-        ("human", "{input}"),
-    ]
-)
-history_aware_retriever = create_history_aware_retriever(
-    llm, retriever, contextualize_q_prompt
-)
-
-"""### **part 10 :Creating Mech-bot QA Chain with LangChain**"""
-
-from langchain.chains.combine_documents import create_stuff_documents_chain
-system_prompt = """
-You are Mech-bot, a highly knowledgeable car mechanic assistant. Your expertise covers a wide range of automotive issues, including diagnostics, repairs, maintenance, and technical advice. Please answer the question using only the information provided in the context:
-
-If the answer is not available in the context, respond with 'I'm sorry, I don't have enough information to answer that question.'
-
-If the question is 'Who are you?' or similar, respond with 'I am Mech-bot, a specialized assistant in car mechanics. Please ask questions related to car mechanics, and I'll do my best to assist you.'
-
-If the question is unrelated to car mechanics, respond with 'I'm sorry, I specialize in car mechanics. Please ask questions related to car mechanics, and I'll do my best to assist you.'
-
-{context}
-</s>
-"""
-qa_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", system_prompt),
-        MessagesPlaceholder("chat_history"),
-        ("human", "{input}"),
-    ]
-)
-question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
-
-from langchain.chains import create_retrieval_chain
-rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
-
-import warnings
-
-# Ignore all warnings
-warnings.filterwarnings('ignore')
-
-"""### **part 11: Moderating Chat Messages with a Template and Guard Model**"""
-
-@weave.op
-def moderate_with_template(chat):
-    input_ids = guard_tokenizer.apply_chat_template(chat, return_tensors="pt")
-    output = guard_model.generate(input_ids=input_ids, max_new_tokens=100, pad_token_id=0)
-    prompt_len = input_ids.shape[-1]
-    return guard_tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
-
-"""# Difining the evaluation and the monitoring functions"""
-
-from ragas import evaluate
-from datasets import Dataset
-from ragas.metrics import (
-    answer_relevancy
-)
-
-emb= HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5')
-
-from langchain_together import Together
-from getpass import getpass
-
-
-# api_key = getpass("Enter your Together AI API key: ")
-
-eval_llm = Together( # using together ai for evaluation
-    model="meta-llama/Meta-Llama-3-8B-Instruct-Turbo",
-    together_api_key='b8c80a28c275fc6e52e89964a81e68e82e5d1ce6471836c9233a7502d9c1343d'
-)
-
-def start_monitor():
-    run = wandb.init(
-    project="Mech-Bot-Evaluation",
-    # name=f"run_{}",
-)
-    return run
-
-@weave.op
-def evaluate_response(question, answer, context):
-  context_contents = [item.page_content for item in context]
-  context_contents
-  output = Dataset.from_dict({"question": [question], "answer": [answer], 'contexts': [context_contents]})
-  score = evaluate(output, llm = eval_llm, embeddings = emb ,metrics=[answer_relevancy])
-  return score
-
-"""### **part 12: Question Answering Function with Content Moderation**
-
-This function, **answer_question**, answers user questions while ensuring content appropriateness through moderation checks.
-
-**Moderate User Question:** Checks if the question is safe. If not, it returns an apologetic message.
-
-**Generate AI Response:** Uses the RAG chain to generate an answer if the question is safe.
-
-**Update and Moderate Chat History:** Adds the question and answer to the chat history and checks if the AI response is appropriate. If not, it apologizes and indicates an attempt to generate a better answer.
-"""
-
-@weave.op
-def invoking(question, chat_history):
-  return rag_chain.invoke({"input": question, "chat_history": chat_history})
-
-from langchain_core.messages import AIMessage, HumanMessage
-chat_history = []
-@weave.op
-def answer_question(question, history):
-    run = start_monitor()  # Start monitoring
-       # Check if the question is safe using Llama guard
-    chat = [ {"role": "user", "content": question} ]
-
-    if not moderate_with_template(chat) == 'safe':
-        return "I'm sorry, but I can't respond to that question as it may contain inappropriate content."
-
-    ai_msg = invoking(question, chat_history)  # Generate AI response
-    chat_history.extend(
-        [
-            HumanMessage(content=question),
-            AIMessage(content=ai_msg["answer"]),
-        ]
-    )
-    system_response = [
-    {"role": "user", "content": question},
-    {"role": "assistant", "content": ai_msg["answer"]},
-]
-    if not moderate_with_template(system_response) == 'safe':
-        return "I generated a response, but it may contain inappropriate content. Let me try again with a more appropriate answer."
-    else:
-
-      # Evaluate the response
-      score = evaluate_response(question, ai_msg["answer"], ai_msg['context'])
-      if score['answer_relevancy'] < 0.7:
-        a = score['answer_relevancy']
-        run.alert(title='Low Answer Relevancy',
-        text=f'Accuracy {a} is below the acceptable theshold')
-      # log the run
-      wandb.log(score.to_pandas().to_dict())
-      run.finish()
-      return ai_msg["answer"]
-
-"""### **part 13: interface**
-
-"""
-
-import gradio as gr
-
-# Create the Chat interface
-iface = gr.ChatInterface(
-    answer_question,  # Use the improved answer_question function
-    title="Mech-bot: Your Car Mechanic Assistant",
-    description="Ask any car mechanic-related questions, and Mech-bot will try its best to assist you.",
-    submit_btn="Ask",
-    clear_btn="Clear Chat"
-)
-
-# Launch the Gradio interface
-iface.launch(debug=True)