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import gradio
from transformers import pipeline
# Initialize the Hugging Face model
model = pipeline(model='google/flan-t5-base')
# Define the chatbot function
def chatbot(input_text):
prompt = f"Give the answer of the given input in context from the bhagwat geeta. give suggestions to user which are based upon the meanings of shlok in bhagwat geeta, input = {input_text}"
# Generate a response from the Hugging Face model
response = model(prompt, max_length=250, do_sample=True)[0]['generated_text'].strip()
# Return the bot response
return response
# Define the Gradio interface
gradio_interface = gradio.Interface(
fn=chatbot,
inputs='text',
outputs='text',
title='Chatbot',
description='A weird chatbot conversations experience.',
examples=[
['Hi, how are you?']
]
)
# Launch the Gradio interface
gradio_interface.launch()
# from dotenv import load_dotenv
# from langchain import HuggingFaceHub, LLMChain
# from langchain import PromptTemplates
# import gradio
# load_dotenv()
# os.getenv('HF_API')
# hub_llm = HuggingFaceHub(repo_id='facebook/blenderbot-400M-distill')
# prompt = prompt_templates(
# input_variable = ["question"],
# template = "Answer is: {question}"
# )
# hub_chain = LLMChain(prompt=prompt, llm=hub_llm, verbose=True)
# Sample code for AI language model interaction
# from transformers import GPT2Tokenizer, GPT2LMHeadModel
# import gradio
# def simptok(data):
# # Load pre-trained model and tokenizer (using the transformers library)
# model_name = "gpt2"
# tokenizer = GPT2Tokenizer.from_pretrained(model_name)
# model = GPT2LMHeadModel.from_pretrained(model_name)
# # User input
# user_input = data
# # Tokenize input
# input_ids = tokenizer.encode(user_input, return_tensors="pt")
# # Generate response
# output = model.generate(input_ids, max_length=50, num_return_sequences=1)
# response = tokenizer.decode(output[0], skip_special_tokens=True)
# return response
# def responsenew(data):
# return simptok(data)
# from hugchat import hugchat
# import gradio as gr
# import time
# # Create a chatbot connection
# chatbot = hugchat.ChatBot(cookie_path="cookies.json")
# # New a conversation (ignore error)
# id = chatbot.new_conversation()
# chatbot.change_conversation(id)
# def get_answer(data):
# return chatbot.chat(data)
# gradio_interface = gr.Interface(
# fn = get_answer,
# inputs = "text",
# outputs = "text"
# )
# gradio_interface.launch()
# gradio_interface = gradio.Interface(
# fn = responsenew,
# inputs = "text",
# outputs = "text"
# )
# gradio_interface.launch()
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