Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load trained model
+classifier = pipeline(
+    "text-classification",
+    model="King-8/request-classifier",
+    tokenizer="King-8/request-classifier",
+    return_all_scores=True
+)
+
+# Label mapping (MUST match training)
+id_to_label = {
+    0: "administrative_action",
+    1: "attendance",
+    2: "check_in",
+    3: "clarification",
+    4: "general_chat",
+    5: "technical_help"
+}
+
+def classify_request(role, context, request):
+    text = f"Role: {role} | Context: {context} | Request: {request}"
+    
+    outputs = classifier(text)[0]
+    
+    # Get highest scoring label
+    best = max(outputs, key=lambda x: x["score"])
+    
+    return {
+        "Predicted intent": best["label"],
+        "Confidence": round(best["score"], 3)
+    }
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Internship Request Classifier")
+    gr.Markdown(
+        "This demo uses a fine-tuned transformer model to classify internship-related requests "
+        "into routing categories such as attendance, check-ins, technical help, and more."
+    )
+
+    role = gr.Dropdown(
+        ["student", "parent", "supervisor", "admin"],
+        label="User Role"
+    )
+
+    context = gr.Textbox(
+        label="Conversation Context",
+        placeholder="e.g., No prior context or Earlier discussion about check-ins"
+    )
+
+    request = gr.Textbox(
+        label="User Request",
+        placeholder="e.g., I missed the Zoom meeting this morning"
+    )
+
+    output = gr.JSON(label="Model Output")
+
+    submit = gr.Button("Classify Request")
+
+    submit.click(
+        classify_request,
+        inputs=[role, context, request],
+        outputs=output
+    )
+
+demo.launch()