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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# 1) Define pastel gradient CSS
css = """
.gradio-container {
background: linear-gradient(to right, #FFDEE9, #B5FFFC);
}
"""
title = "Bonjour Dans le chat du consentement"
# 2) Load the Mistral model & tokenizer from HF Hub
model_id = "mistralai/Mistral-7B-Instruct-v0.3"
# If you're on a GPU Space, you can do:
# device_map = "auto"
# torch_dtype = torch.bfloat16
# If you're on a CPU-only Space, remove those arguments or set device_map="cpu"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto", # "auto" if you have GPU
torch_dtype=torch.bfloat16, # for GPU. Remove or use float32 on CPU
trust_remote_code=True
)
# 3) Create a text-generation pipeline
generate_text = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_length=512, # adjust as needed
temperature=0.7, # adjust as needed
do_sample=True
)
def mistral_inference(prompt):
"""
Passes user prompt to the pipeline and returns the generated text.
We'll strip any special tokens and limit the output.
"""
# The pipeline returns a list of dicts [{"generated_text": "..."}]
outputs = generate_text(prompt)
text_out = outputs[0]["generated_text"]
return text_out
# 4) Build the Gradio interface with a pastel background & greeting
with gr.Blocks(css=css) as demo:
gr.Markdown(f"<h1 style='text-align:center;'>{title}</h1>")
user_input = gr.Textbox(label="Entrez votre message ici:", lines=3)
output = gr.Textbox(label="Réponse du Modèle:", lines=5)
send_button = gr.Button("Envoyer")
# Link the button to the inference function
send_button.click(fn=mistral_inference, inputs=user_input, outputs=output)
# 5) Launch the app
if __name__ == "__main__":
demo.launch()
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