Create app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+from textwrap import fill
+
+# Load the finetuned model and tokenizer
+last_checkpoint = "model/checkpoint-1000"
+finetuned_model = T5ForConditionalGeneration.from_pretrained(last_checkpoint)
+tokenizer = T5Tokenizer.from_pretrained(last_checkpoint)
+
+def answer_question(question):
+    inputs = "Answer this question truthfully: " + question
+    tokenized_inputs = tokenizer(inputs, return_tensors="pt", padding=True, truncation=True)
+    outputs = finetuned_model.generate(**tokenized_inputs)
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Assuming 'actual' answer is predefined for demonstration
+    actual = "Very low Mg2+ levels correspond to low PTH levels which in turn results in low Ca2+ levels."
+    
+    return fill(answer, width=80), fill(actual, width=80)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=answer_question,
+    inputs="text",
+    outputs=["text", "text"],
+    title="Medical Question Answering",
+    description="Enter a medical question to get a truthful answer from the finetuned T5 model.",
+    examples=[["What is the relationship between very low Mg2+ levels, PTH levels, and Ca2+ levels?"]]
+)
+
+# Launch the app
+iface.launch()