Update app.py (#1)

- Update app.py (3e36f5267b0b9100d42034e1b038175abe3bef4e)


Co-authored-by: Kadir Nar <[email protected]>

app.py

@@ -19,8 +19,8 @@ def load_model(model_name="BioGPT"):
       "BioGPT-QA-PubMedQA-BioGPT":"microsoft/BioGPT-Large-PubMedQA"
   }
 
-  tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
-  model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
+  tokenizer = BioGptTokenizer.from_pretrained(model_name_map[model_name])
+  model = BioGptForCausalLM.from_pretrained(model_name_map[model_name])
   return tokenizer, model
 
 
@@ -39,18 +39,25 @@ def get_beam_output(sentence, selected_model, min_len=100,max_len=512, n_beams=1
 
 inputs = [
     gr.inputs.Textbox(label="prompt", lines=5, default="Bicalutamide"),
-    gr.Dropdown(model_names, value="microsoft/biogpt", label="selected_model"),
+    gr.Dropdown(model_names, value="BioGPT", label="selected_model"),
     gr.inputs.Slider(1, 500, 1, default=100, label="min_len"),
     gr.inputs.Slider(1, 2048, 1, default=1024, label="max_len"),
     gr.inputs.Slider(1, 10, 1, default=5, label="num_beams")
     ]
+
 outputs = gr.outputs.Textbox(label="output")
 
+examples = [
+    ["Bicalutamide", "BioGPT", 25, 100, 5],
+    ["Janus kinase 3 (JAK-3)", "BioGPT", 25, 100, 5],
+    ["Apricitabine", "BioGPT", 25, 100, 5],
+]
+
 iface = gr.Interface(
   fn=get_beam_output, 
   inputs=inputs,
   outputs=outputs,
-  examples=[["Bicalutamide"], ["Janus kinase 3 (JAK-3)"], ["Apricitabine"], ["Xylazine"], ["Psoralen"], ["CP-673451"]]
+  examples=examples,
+  title="BioGPT: generative pre-trained transformer for biomedical text generation and mining"
 )
-
 iface.launch(debug=True, enable_queue=True)