app.py

import gradio as gr
# from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import transformers
import torch
from huggingface_hub import login
from langchain_community.llms import HuggingFacePipeline
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
# login(token=token)

def greet(name):
    return str(int(name)+10)



# Load model directly


tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
pipe = pipeline(
    "text2text-generation",
    model=model,
    tokenizer=tokenizer,
    max_length=512,
    temperature=0.5,
    top_p=0.95,
    repetition_penalty=1.15
)

local_llm = HuggingFacePipeline(pipeline=pipe)

def get_llama_response(prompt: str) -> None:
    """
    Generate a response from the Llama model.

    Parameters:
        prompt (str): The user's input/question for the model.

    Returns:
        None: Prints the model's response.
    """
    sequences = llama_pipeline(
        prompt,
        do_sample=True,
        top_k=10,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id,
        max_length=256,
        truncation=True
    )
    print("Chatbot:", sequences[0]['generated_text'])



prompt = 'I liked "Breaking Bad" and "Band of Brothers". Do you have any recommendations of other shows I might like?\n'
get_llama_response(prompt)
print('hhh')
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()