Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+# Manually load tokenizer from base model
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("King-8/interview_statements")
+
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+def classify_statement(statement):
+    prediction = classifier(statement)[0]
+    label = prediction["label"]
+    score = round(prediction["score"] * 100, 2)
+    return f"Category: {label} (Confidence: {score}%)"
+
+gr.Interface(
+    fn=classify_statement,
+    inputs=gr.Textbox(label="Enter a community statement"),
+    outputs=gr.Textbox(label="Predicted Category"),
+    title="Interview Statement Categorizer",
+    description="This app predicts the category of a community statement based on trained data from community interviews."
+).launch()