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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load model and tokenizer from Hugging Face
model_name = "visalkao/sentiment-analysis-french" # Replace with your model's name
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Prediction function
def classify_email(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
predictions = outputs.logits.argmax(axis=-1).item()
return "Avis négatif" if predictions == 0 else "Avis positif"
css = """
.centered-col {
margin: 0 auto;
width: 30%;
}
"""
with gr.Blocks(css=css) as demo:
# Title and description
gr.Markdown("## Analyse du sentiment des avis des clients")
gr.Markdown("Écrire un avis sur un produit.")
# Input row
with gr.Row():
with gr.Column(elem_classes="centered-col"):
input_text = gr.Textbox(label="Input", placeholder="Avis...")
# Output row
with gr.Row():
with gr.Column(elem_classes="centered-col"):
output_text = gr.Textbox(label="Output")
# Submit button (full-width by default)
btn = gr.Button("Envoyer")
btn.click(fn=classify_email, inputs=input_text, outputs=output_text)
demo.launch()
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