Add app

app.py

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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()

requirements.txt

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+gradio
+transformers
+torch