Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# 🔹 Load your model from Hugging Face Hub
+model_name = "ShubhamCoder01/stress-detection-model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# 🔹 Prediction function
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probs).item()
+    confidence = probs[0][predicted_class].item()
+
+    return {
+        "Predicted Label": str(predicted_class),
+        "Confidence": round(confidence, 4)
+    }
+
+# 🔹 Gradio Interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=3, placeholder="Enter a sentence..."),
+    outputs="json",
+    title="Stress Detection Model",
+    description="Enter some text and the model will predict if it indicates stress or not."
+)
+
+iface.launch()