Create app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+
+# Load the model using the pipeline
+pipe = pipeline("text-classification", model="AliArshad/Severity_Predictor")
+from transformers import pipeline
+import gradio as gr
+
+# Load the model using the pipeline
+pipe = pipeline("text-classification", model="AliArshad/Severity_Predictor")
+
+# Function to predict severity and return confidence score
+def predict_severity(text):
+    # Get prediction from the pipeline
+    prediction = pipe(text)
+
+    # Interpret the label and get the confidence score
+    label = prediction[0]['label']
+    confidence = prediction[0]['score']
+    severity = "Severe" if label == "LABEL_1" else "Non-Severe"
+
+    # Return severity and confidence as separate outputs
+    return severity, confidence
+
+# Define the Gradio interface with a title, specific placeholder message, and a progress bar for confidence
+iface = gr.Interface(
+    fn=predict_severity,
+    inputs=gr.Textbox(lines=2, placeholder="Please Enter Bug Report Summary"),
+    outputs=[
+        gr.Textbox(label="Prediction"),
+        gr.Number(label="Confidence", precision=2)
+    ],
+    title="GPT-2 Based Severity Prediction"
+)
+
+# Launch the interface
+iface.launch()