Update app.py

app.py

@@ -5,6 +5,7 @@ from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
 
 model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)  # 2 classes : positif et négatif
 
 ds = load_dataset("stanfordnlp/sst2")
 
@@ -18,6 +19,39 @@ def encode(examples):
 sst2_dataset = sst2_dataset.map(encode, batched=True)
 sst2_dataset = sst2_dataset.map(lambda examples: {"labels": examples["label"]}, batched=True)
 
-
-
+training_args = TrainingArguments(
+    per_device_train_batch_size=8,
+    evaluation_strategy="epoch",
+    logging_dir="./logs",
+    output_dir="./results",
+    num_train_epochs=3,
+)
+
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=encoded_dataset["train"],
+    eval_dataset=encoded_dataset["test"],
+)
+
+import os
+if not os.path.exists("./fine_tuned_model"):
+    trainer.train()
+    # Sauvegarder le modèle fine-tuné et le tokenizer
+    model.save_pretrained("./fine_tuned_model")
+    tokenizer.save_pretrained("./fine_tuned_model")
+else:
+    # Charger le modèle fine-tuné
+    model = BertForSequenceClassification.from_pretrained("./fine_tuned_model")
+    tokenizer = BertTokenizer.from_pretrained("./fine_tuned_model")
+
+
+sentiment_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+def generate_response(message):
+    result = sentiment_analysis(message)[0]
+    return f"Label: {result['label']}, Score: {result['score']}"
+
+gr.ChatInterface(fn=generate_response).launch()