Update app.py

app.py

@@ -55,9 +55,9 @@ def gradio_generate_text(prompt, max_length=100, num_return_sequences=1, top_p=0
     generated_text = generate_text(tokenizer, model, device, prompt, max_length, num_return_sequences, top_p, temperature)
     return generated_text
 
-def gradio_generate_text(prompt, max_length, num_sequences, top_p, temperature):
-    # Placeholder for your text generation logic
-    return f"Generated text based on: {prompt}"
+# def gradio_generate_text(prompt, max_length, num_sequences, top_p, temperature):
+#     # Placeholder for your text generation logic
+#     return f"Generated text based on: {prompt}"
 
 # Ensure the models directory exists
 if not os.path.exists('models'):
@@ -68,10 +68,8 @@ if not os.path.exists('models/vi-medical-t5-finetune-qa'):
     run_shell_command('cd models && git clone https://huggingface.co/danhtran2mind/vi-medical-t5-finetune-qa && cd ..')
 
 # Load the trained model and tokenizer
-print('dqwdqqqqqqqqqqqqqqqqq')
 model_path = "models/vi-medical-t5-finetune-qa"
 tokenizer, model, device = load_model_and_tokenizer(model_path)
-print('dqwdqqqqqqqqqqqqqqqqq_2')
 # Create Gradio interface
 
 
@@ -80,8 +78,8 @@ iface = gr.Interface(
     fn=gradio_generate_text,
     inputs=[
         gr.Textbox(lines=5, label="Input Prompt"),
-        gr.Slider(minimum=10, maximum=500, value=100, label="Max Length"),
-        gr.Slider(minimum=1, maximum=10, value=1, label="Number of Sequences"),
+        gr.Slider(minimum=10, maximum=768, value=32, label="Max Length"),
+        gr.Slider(minimum=1, maximum=5, value=1, label="Number of Sequences"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top-p Sampling"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature")
     ],