test01.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# Load the model and tokenizer
model_name = "Reverb/Mistral-7B-LoreWeaver"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Initialize the pipeline
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)

def generate_story(prompt):
    # Generate a response using the model
    responses = generator(prompt, max_length=200, num_return_sequences=1)
    return responses[0]['generated_text']

# Define the Gradio interface
iface = gr.Interface(
    fn=generate_story,
    inputs=gr.Textbox(lines=5, placeholder="Enter your prompt here..."),
    outputs=gr.Textbox(label="Generated Story"),
    title="Mistral-7B-LoreWeaver Story Generator",
    description="Enter a prompt to generate a narrative text using the Mistral-7B-LoreWeaver model."
)

# Launch the interface
iface.launch()