app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "mistralai/Codestral-22B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_text(prompt, max_length=1000, min_length=50, temperature=0.1):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs['input_ids'],
        max_length=max_length,
        min_length=min_length,
        temperature=temperature
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

interface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
        gr.inputs.Slider(minimum=10, maximum=2000, default=1000, label="Max Length"),
        gr.inputs.Slider(minimum=10, maximum=100, default=50, label="Min Length"),
        gr.inputs.Slider(minimum=0.1, maximum=1, default=0.1, label="Temperature")
    ],
    outputs="text",
    title="Text Generation with Mistralai",
    description="Generate text using the mistralai/Codestral-22B-v0.1 model."
)

if __name__ == "__main__":
    interface.launch()