Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+model_name = "himanshubeniwal/bert_lf_bond"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+def predict_bond(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True)
+    
+    # Get model prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        predicted_class = torch.argmax(predictions).item()
+        confidence = predictions[0][predicted_class].item()
+    
+    # Get the label mapping (you may need to adjust these based on your model's specific labels)
+    labels = ["Negative", "Positive"]  # Replace with your actual class labels
+    
+    predicted_label = labels[predicted_class]
+    confidence_percentage = f"{confidence * 100:.2f}%"
+    
+    return {
+        "Predicted Class": predicted_label,
+        "Confidence": confidence_percentage
+    }
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_bond,
+    inputs=gr.Textbox(lines=5, label="Enter bond-related text"),
+    outputs=gr.JSON(label="Prediction Results"),
+    title="James Bond Classification Model",
+    description="This model classifies bond-related text using a fine-tuned BERT model.",
+    examples=[
+        ["I love the James Bond movies!"],
+        ["I hate the James Bond movies. "]
+    ]
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()