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# 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)
print(response)
def responsenew(data):
return simptok(data)
gradio_interface = gradio.Interface(
fn = responsenew,
inputs = "text",
outputs = "text"
)
gradio_interface.launch()
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