Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""gradio_deploy.ipynb
+
+Automatically generated by Colaboratory.
+
+"""
+import os
+import gradio as gr
+from PIL import Image
+from timeit import default_timer as timer
+from tensorflow import keras
+import numpy as np
+
+loaded_model = AutoModelWithLMHead.from_pretrained("runaksh/medquad-finetuned-gpt2"))
+loaded_tokenizer = AutoTokenizer.from_pretrained("runaksh/medquad-finetuned-gpt2")
+
+def generate_query_response(prompt, max_length=200):
+
+    model = loaded_model
+    tokenizer = loaded_tokenizer
+
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+    attention_mask = torch.ones_like(input_ids)
+    pad_token_id = tokenizer.eos_token_id
+
+    output = model.generate(
+        input_ids,
+        max_length=max_length,
+        num_return_sequences=1,
+        attention_mask=attention_mask,
+        pad_token_id=pad_token_id
+    )
+
+    return tokenizer.decode(output[0])
+
+# Gradio elements
+
+# Input from user
+in_prompt = gradio.inputs.Textbox(lines=2, label='Enter the question')
+in_max_length = gradio.inputs.Number(label='Enter the max length')
+
+# Output response
+out_response = gradio.outputs.Textbox(label='Answer')
+
+# Gradio interface to generate UI link
+iface = gradio.Interface(fn=generate_query_response,
+                         inputs = [in_prompt,in_max_length],
+                         outputs = out_response
+                         )
+
+iface.launch(debug = True)
+