app.py

import gradio as gr
from langchain_community.document_loaders import CSVLoader  # Changed import
from langchain_community.vectorstores import FAISS  # Changed import
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFaceLLM  # Adjusted for correct instantiation
import warnings
from huggingface_hub import login
import os
from transformers import pipeline

# Initialize the LLM using pipeline
llm = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct")  # Adjusted initialization

# Load CSV file
loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column='prompt')
data = loader.load()

# Suppress warnings
warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated")
warnings.filterwarnings("ignore", category=FutureWarning, message="`resume_download` is deprecated")

# Embedding model
model_name = "BAAI/bge-m3"
instructor_embeddings = HuggingFaceLLM(model_name=model_name)  # Adjusted for correct instantiation

# Create FAISS vector store from documents
vectordb = FAISS.from_documents(documents=data, embedding=instructor_embeddings)
retriever = vectordb.as_retriever()

# Define the prompt template
prompt_template = """Given the following context and a question, generate an answer based on the context only.
In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at [email protected]" Don't try to make up an answer.
CONTEXT: {context}
QUESTION: {question}"""

PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question"]
)

# Initialize the RetrievalQA chain
chain = RetrievalQA.from_chain_type(llm=llm,  # Adjusted initialization
                                    chain_type="stuff",
                                    retriever=retriever,
                                    input_key="query",
                                    return_source_documents=True,
                                    chain_type_kwargs={"prompt": PROMPT})

# Define the chat response function
def chatresponse(message, history):
    output = chain(message)
    return output['result']

# Launch the Gradio chat interface
gr.ChatInterface(chatresponse).launch()


# import gradio as gr
# # from langchain.llms import GooglePalm
# from langchain_google_genai import GoogleGenerativeAI
# from langchain.document_loaders.csv_loader import CSVLoader
# from langchain_huggingface import HuggingFaceEmbeddings
# from langchain.vectorstores import FAISS
# from langchain.prompts import PromptTemplate
# from langchain.chains import RetrievalQA
# import warnings
# from huggingface_hub import login
# import os


# from transformers import pipeline
# llm = pipeline("feature-extraction", model="mixedbread-ai/mxbai-embed-large-v1") 

# # from transformers import AutoModel
# # llm = AutoModel.from_pretrained("Alibaba-NLP/gte-large-en-v1.5", trust_remote_code=True) 

# # LLAMA
# # from transformers import AutoModelForCausalLM, AutoTokenizer
# # from transformers import pipeline

# # hf_token = os.environ['llama_token']

# # login(token=hf_token)

# # llm = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct")
# # llm = pipeline("text-generation", model = "meta-llama/Meta-Llama-3-70B-Instruct")

# # MISTRAL
# # llm = pipeline("text-generation", model="mistralai/Mixtral-8x22B-Instruct-v0.1")


# # TO USE GOOGLE MODELS
# # api_key = "AIzaSyCdM_aAIsW_nPbjarOF83mbX1_z1cVX2_M"

# # llm = GoogleGenerativeAI(model="models/text-bison-001", google_api_key=api_key)
# # llm = GooglePalm(google_api_key = api_key, temperature=0.7)

# # LOADING CSV FILE
# loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt')
# data = loader.load()

# # SUPPRESSING WARNINGS
# warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated")
# warnings.filterwarnings("ignore", category=FutureWarning, message="`resume_download` is deprecated")


# # EMBEDDING MODEL
# model_name = "BAAI/bge-m3"
# instructor_embeddings = HuggingFaceEmbeddings(model_name=model_name)

# # Create FAISS vector store from documents
# vectordb = FAISS.from_documents(documents=data, embedding=instructor_embeddings)
# retriever = vectordb.as_retriever()

# prompt_template = """Given the following context and a question, generate an answer based on the context only.

# In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
# If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
# If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at [email protected]" Don't try to make up an answer.

# CONTEXT: {context}

# QUESTION: {question}"""

# PROMPT = PromptTemplate(
#     template = prompt_template, input_variables = ["context", "question"]
# )


# chain = RetrievalQA.from_chain_type(llm = llm,
#             chain_type="stuff",
#             retriever=retriever,
#             input_key="query",
#             return_source_documents=True,
#             chain_type_kwargs = {"prompt": PROMPT})

# def chatresponse(message, history):
#     output = chain(message)
#     return output['result']

# gr.ChatInterface(chatresponse).launch()



# import gradio as gr

# # from langchain.llms import GooglePalm
# # from langchain.document_loaders.csv_loader import CSVLoader
# # from langchain_huggingface import HuggingFaceEmbeddings
# # from langchain.vectorstores import FAISS


# from langchain_community.llms import GooglePalm
# from langchain_community.document_loaders import CSVLoader
# from langchain_community.vectorstores import FAISS
# from langchain_huggingface import HuggingFaceEmbeddings


# api_key = "AIzaSyCdM_aAIsW_nPbjarOF83mbX1_z1cVX2_M"

# llm = GooglePalm(google_api_key = api_key, temperature=0.7)


# loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt')
# data = loader.load()


# instructor_embeddings = HuggingFaceEmbeddings(model_name = "BAAI/bge-m3")
# vectordb = FAISS.from_documents(documents = data, embedding = instructor_embeddings)

# retriever = vectordb.as_retriever()

# from langchain.prompts import PromptTemplate

# prompt_template = """Given the following context and a question, generate an answer based on the context only.

# In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
# If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
# If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at [email protected]" Don't try to make up an answer.

# CONTEXT: {context}

# QUESTION: {question}"""

# PROMPT = PromptTemplate(
#     template = prompt_template, input_variables = ["context", "question"]
# )

# from langchain.chains import RetrievalQA

# chain = RetrievalQA.from_chain_type(llm = llm,
#             chain_type="stuff",
#             retriever=retriever,
#             input_key="query",
#             return_source_documents=True,
#             chain_type_kwargs = {"prompt": PROMPT})

# def chatresponse(message, history):
#     output = chain(message)
#     return output['result']

# gr.ChatInterface(chatresponse).launch()


# import gradio as gr
# from langchain.llms import GooglePalm

# api_key = "AIzaSyCdM_aAIsW_nPbjarOF83mbX1_z1cVX2_M"

# llm = GooglePalm(google_api_key = api_key, temperature=0.7)

# from langchain.document_loaders.csv_loader import CSVLoader

# loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt')
# data = loader.load()

# from langchain_huggingface import HuggingFaceEmbeddings
# from langchain.vectorstores import FAISS

# # instructor_embeddings = HuggingFaceEmbeddings(model_name = "Alibaba-NLP/gte-Qwen2-7B-instruct") # best model <-- but too big
# instructor_embeddings = HuggingFaceEmbeddings(model_name = "BAAI/bge-m3")
# # instructor_embeddings = HuggingFaceEmbeddings()

# vectordb = FAISS.from_documents(documents = data, embedding = instructor_embeddings)

# # e = embeddings_model.embed_query("What is your refund policy")

# retriever = vectordb.as_retriever()

# from langchain.prompts import PromptTemplate

# prompt_template = """Given the following context and a question, generate an answer based on the context only.

# In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
# If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
# If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at [email protected]" Don't try to make up an answer.

# CONTEXT: {context}

# QUESTION: {question}"""

# PROMPT = PromptTemplate(
#     template = prompt_template, input_variables = ["context", "question"]
# )

# from langchain.chains import RetrievalQA

# chain = RetrievalQA.from_chain_type(llm = llm,
#             chain_type="stuff",
#             retriever=retriever,
#             input_key="query",
#             return_source_documents=True,
#             chain_type_kwargs = {"prompt": PROMPT})

# # Load your LLM model and necessary components
# # Assume `chain` is a function defined in your notebook that takes a query and returns the output as shown
# # For this example, we'll assume the model and chain function are already available

# def chatbot(query):
#     response = chain(query)
#     # Extract the 'result' part of the response
#     result = response.get('result', 'Sorry, I could not find an answer.')
#     return result

# # Define the Gradio interface
# iface = gr.Interface(
#     fn=chatbot,  # Function to call
#     inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your question here..."),  # Input type
#     outputs="text",  # Output type
#     title="Hugging Face LLM Chatbot",
#     description="Ask any question related to the documents and get an answer from the LLM model.",
# )

# # Launch the interface
# iface.launch()

# # Save this file as app.py and push it to your Hugging Face Space repository

# # import gradio as gr

# # def greet(name, intensity):
# #     return "Hello, " + name + "!" * int(intensity)

# # demo = gr.Interface(
# #     fn=greet,
# #     inputs=["text", "slider"],
# #     outputs=["text"],
# # )

# # demo.launch()


# # import gradio as gr
# # from huggingface_hub import InferenceClient

# # """
# # For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# # """
# # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


# # def respond(
# #     message,
# #     history: list[tuple[str, str]],
# #     system_message,
# #     max_tokens,
# #     temperature,
# #     top_p,
# # ):
# #     messages = [{"role": "system", "content": system_message}]

# #     for val in history:
# #         if val[0]:
# #             messages.append({"role": "user", "content": val[0]})
# #         if val[1]:
# #             messages.append({"role": "assistant", "content": val[1]})

# #     messages.append({"role": "user", "content": message})

# #     response = ""

# #     for message in client.chat_completion(
# #         messages,
# #         max_tokens=max_tokens,
# #         stream=True,
# #         temperature=temperature,
# #         top_p=top_p,
# #     ):
# #         token = message.choices[0].delta.content

# #         response += token
# #         yield response

# # """
# # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# # """
# # demo = gr.ChatInterface(
# #     respond,
# #     additional_inputs=[
# #         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
# #         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
# #         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
# #         gr.Slider(
# #             minimum=0.1,
# #             maximum=1.0,
# #             value=0.95,
# #             step=0.05,
# #             label="Top-p (nucleus sampling)",
# #         ),
# #     ],
# # )


# # if __name__ == "__main__":
# #     demo.launch()