app.py

import gradio as gr

import langchain, langchain_huggingface

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

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()