Customize gradio app

app.py

@@ -1,7 +1,47 @@
+"""
+M6_NB_MiniProject_1_PartB_Deploy_Medical_Q&A_GPT2.ipynb
+Original file in google drive
+"""
+import os
 import gradio as gr
+from transformers import AutoModelWithLMHead, AutoTokenizer
 
-def greet(name):
-    return "Hello " + name + "!!"
+def generate_response(model, tokenizer, prompt, max_length=512):
+    # YOUR CODE HERE ...
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")      # 'pt' for returning pytorch tensor
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    # Create the attention mask and pad token id
+    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], skip_special_tokens=True)
+
+def generate_query_response(prompt, max_length=200):
+    # Load your model from hub
+    model = AutoModelWithLMHead.from_pretrained("vanim/chatgpt2-medical-QnA")
+    # Load your tokenizer from hub
+    tokenizer = AutoTokenizer.from_pretrained("vanim/chatgpt2-medical-QnA")
+
+    return generate_response(model, tokenizer, prompt, max_length)
+
+
+# Create title, description and article strings
+title = "Medical QnA chat bot"
+description = "ChatGPT2 based Medical Q and A demo"
+
+demo = gr.Interface(fn=generate_query_response, 
+                    inputs = "text",
+                    outputs = "text",
+                    title=title,
+                    description=description,)
+
+#Launch the demo
+demo.launch()