Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio
+import torch
+from transformers import AutoModelWithLMHead, AutoTokenizer
+
+# Load model directly
+
+
+tokenizer = AutoTokenizer.from_pretrained("raghavdw/finedtuned_gpt2_medQA_model")
+model = AutoModelForCausalLM.from_pretrained("raghavdw/finedtuned_gpt2_medQA_model")
+
+# Function for response generation
+
+def generate_query_response(prompt, max_length=200):
+
+    model = loaded_model
+    tokenizer = loaded_tokenizer
+
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    input_ids = input_ids.to(device)
+    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)
+    
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Gradio elements
+
+# Input from user
+in_prompt = gradio.Textbox(label="Enter your prompt")
+
+# Output response
+in_max_length = 200
+
+
+# Output response
+out_response = gradio.Textbox(label="Generated Response")
+
+# Gradio
+iface = gradio.Interface(fn=generate_query_response,
+                         inputs=[in_prompt],
+                         outputs=out_response,
+                         title =  "Medical Summary",
+                         description = "using fine-tune medQA gpt-2 model")
+
+
+# YOUR CODE HERE to launch the interface
+iface.launch(share = True)