Upload app.py

app.py

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+# import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+# ---------------------------------------------------
+# 1. Load Model + Tokenizer
+# ---------------------------------------------------
+model_path = r"D:\\Assignment\\fine_tuned_deberta_model"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
+model = AutoModelForSequenceClassification.from_pretrained(model_path, local_files_only=True)
+
+model.eval()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+labels = ["Entailment", "Neutral", "Contradiction"]
+
+# ---------------------------------------------------
+# 2. Prediction Function
+# ---------------------------------------------------
+def predict_nli(premise, hypothesis):
+    if not premise.strip() or not hypothesis.strip():
+        return "Error: Inputs cannot be empty.", None
+
+    text = premise + " [SEP] " + hypothesis
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = torch.softmax(logits, dim=-1)[0].cpu().numpy()
+
+    pred_idx = int(probs.argmax())
+    pred_label = labels[pred_idx]
+
+    prob_dict = {labels[i]: f"{probs[i] * 100:.2f}%" for i in range(len(labels))}
+
+    return pred_label, prob_dict
+
+# ---------------------------------------------------
+# 3. Gradio Interface
+# ---------------------------------------------------
+interface = gr.Interface(
+    fn=predict_nli,
+    inputs=[
+        gr.Textbox(label="Premise", placeholder="Enter the premise sentence..."),
+        gr.Textbox(label="Hypothesis", placeholder="Enter the hypothesis sentence..."),
+    ],
+    outputs=[
+        gr.Label(label="Prediction"),
+        gr.JSON(label="Confidence Scores (%)"),
+    ],
+    title="NLI Model (DeBERTa + CCT Ready)",
+    description="Enter a premise and hypothesis to get Entailment, Neutral, or Contradiction with confidence scores.",
+    flagging_mode="never"     # <-- UPDATED
+)
+
+interface.launch(server_name="0.0.0.0", server_port=7860)