update app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer from local directory
+MODEL_PATH = "./saved_model1"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+
+def predict(text):
+    # Preprocess input
+    inputs = tokenizer(
+        text,
+        padding=True,
+        truncation=True,
+        max_length=512,
+        return_tensors="pt"
+    )
+    
+    # Inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Postprocess output (modify based on your task)
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probabilities).item()
+    
+    return {
+        "text": text,
+        "predicted_class": predicted_class,
+        "probabilities": probabilities.tolist()[0]
+    }
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Input Text", lines=3),
+    outputs=[
+        gr.Textbox(label="Processed Text"),
+        gr.Number(label="Predicted Class"),
+        gr.Label(label="Class Probabilities")
+    ],
+    title="BERT Model Deployment",
+    examples=[["Sample text 1"], ["Another example text"]]
+)
+
+if __name__ == "__main__":
+    demo.launch()