Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, pipeline
+
+# Load the tokenizer
+# model_checkpoint = "roberta-large"
+# tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+
+model_checkpoint = "tyutfyu/roberta-large-2"
+
+
+# Load the text classification pipeline
+pipe = pipeline("text-classification", model=model_checkpoint)
+
+def classify_text(text, question):
+    result = pipe(question, text)
+    if result[0]['label'] == 'LABEL_0':
+        result[0]['label'] = 'yes'
+    elif result[0]['label'] == 'LABEL_1':
+        result[0]['label'] = 'no'
+    return result[0]['label'], result[0]['score']
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_text,
+    inputs=["text", "text"],
+    outputs=["text", "number"],
+    layout="vertical",
+    live=True,
+    title="Get yes/No answer for your medical question",
+    description="Predict if a statement is true or false."
+)
+
+# Launch the Gradio interface
+iface.launch()