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import gradio as gr
from transformers import pipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the Mistral-7B-v0.1 model and tokenizer
model_name = "mistralai/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Load the llama2 LLM model
# model = pipeline("text-generation", model="llamalanguage/llama2", tokenizer="llamalanguage/llama2")
# model = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1", tokenizer="meta-llama/Llama-2-7b-chat-hf")
# Define the chat function that uses the LLM model
# def chat_interface(input_text):
# response = model(input_text, max_length=100, return_full_text=True)[0]["generated_text"]
# response_words = response.split()
# return response_words
# Define the chat function that uses the Mistral-7B-v0.1 model
def chat_interface(input_text):
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Create the Gradio interface
iface = gr.Interface(
fn=chat_interface,
inputs=gr.inputs.Textbox(lines=2, label="Input Text"),
outputs=gr.outputs.Textbox(label="Output Text"),
title="Chat Interface",
description="Enter text and get a response using the LLM model",
live=True # Enable live updates
)
# Launch the interface using Hugging Face Spaces
iface.launch(share=True) |