Simplify Gradio interface for better API compatibility

app.py

@@ -5,22 +5,22 @@ from transformers import T5Tokenizer, T5ForConditionalGeneration
 model = T5ForConditionalGeneration.from_pretrained('t5-small')
 tokenizer = T5Tokenizer.from_pretrained('t5-small')
 
-def generate_clinical_report(input_text, max_length=256, num_beams=4, no_repeat_ngram_size=3, length_penalty=2.0, early_stopping=True):
+def generate_clinical_report(input_text):
     """
-    Generate a clinical report from the input text using the T5 model with configurable parameters.
+    Generate a clinical report from the input text using the T5 model.
     """
     try:
         # Prepare input text
         input_ids = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)
         
-        # Generate report with provided parameters
+        # Generate report
         outputs = model.generate(
             input_ids,
-            max_length=max_length,
-            num_beams=num_beams,
-            no_repeat_ngram_size=no_repeat_ngram_size,
-            length_penalty=length_penalty,
-            early_stopping=early_stopping,
+            max_length=256,
+            num_beams=4,
+            no_repeat_ngram_size=3,
+            length_penalty=2.0,
+            early_stopping=True,
             bad_words_ids=[[tokenizer.encode(word, add_special_tokens=False)[0]] 
                           for word in ['http', 'www', '.com', '.org']]
         )
@@ -31,34 +31,27 @@ def generate_clinical_report(input_text, max_length=256, num_beams=4, no_repeat_
         print(f"Error generating report: {str(e)}")
         return f"Error: {str(e)}"
 
-# Create Gradio interface with API configuration
+# Create Gradio interface
 demo = gr.Interface(
     fn=generate_clinical_report,
-    inputs=[
-        gr.Textbox(
-            lines=8,
-            placeholder="Enter clinical notes here...",
-            label="Clinical Notes"
-        ),
-        gr.Slider(minimum=50, maximum=500, value=256, step=1, label="Max Length"),
-        gr.Slider(minimum=1, maximum=8, value=4, step=1, label="Num Beams"),
-        gr.Slider(minimum=1, maximum=5, value=3, step=1, label="No Repeat Ngram Size"),
-        gr.Slider(minimum=0.1, maximum=5.0, value=2.0, step=0.1, label="Length Penalty"),
-        gr.Checkbox(value=True, label="Early Stopping")
-    ],
-    outputs=gr.Textbox(lines=8, label="Generated Clinical Report"),
+    inputs=gr.Textbox(
+        lines=8,
+        placeholder="Enter clinical notes here...",
+        label="Clinical Notes"
+    ),
+    outputs=gr.Textbox(
+        lines=8,
+        label="Generated Clinical Report"
+    ),
     title="Clinical Report Generator",
     description="Generate professional clinical reports from clinical notes using a T5 model.",
-    allow_flagging="never",
-    analytics_enabled=False
+    allow_flagging="never"
 )
 
-# Launch the app with optimized configuration for Hugging Face Spaces
+# Launch the app
 if __name__ == "__main__":
-    demo.queue()  # Enable queue with default settings
+    demo.queue()
     demo.launch(
         server_name="0.0.0.0",
-        share=True,
-        show_error=True,
-        max_threads=40  # Increase max threads for better performance
+        share=True
     )