Create app.py

app.py

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+app_code = """\
+import gradio as gr
+import torch
+import torch.nn.functional as F
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the fine-tuned SBERT model from Hugging Face
+model_name = "Steph974/SBERT-FineTuned-Classifier"  # Your uploaded model
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Ensure the model is on the correct device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+def predict_similarity(sentence1, sentence2):
+    \"\"\"
+    Predicts the probability of two sentences belonging to the same class (1) or different (0).
+    Returns probability instead of class label.
+    \"\"\"
+    # Tokenize input
+    inputs = tokenizer(sentence1, sentence2, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
+    inputs = {key: value.to(device) for key, value in inputs.items()}  # Move tensors to model device
+
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Get probabilities
+    probabilities = F.softmax(outputs.logits, dim=1).cpu().numpy()[0]
+    proba_same = probabilities[1]  # Probability that sentences are in the same class
+    proba_diff = probabilities[0]  # Probability that sentences are different
+
+    return {
+        "Same Class Probability": round(proba_same * 100, 2),
+        "Different Class Probability": round(proba_diff * 100, 2)
+    }
+
+# Gradio UI
+interface = gr.Interface(
+    fn=predict_similarity,
+    inputs=[
+        gr.Textbox(label="Sentence 1", placeholder="Enter the first sentence..."),
+        gr.Textbox(label="Sentence 2", placeholder="Enter the second sentence...")
+    ],
+    outputs=gr.Label(label="Prediction Probabilities"),
+    title="SBERT Sentence-Pair Similarity",
+    description="Enter two sentences and see how similar they are according to the fine-tuned SBERT model.",
+    theme="huggingface",
+)
+
+# Launch the Gradio app
+interface.launch()
+"""
+
+# Save to app.py
+with open("app.py", "w") as f:
+    f.write(app_code)