Update app.py

app.py

@@ -6,7 +6,7 @@ from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
 tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2")
 model = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2")
 
-# Load a faster speech-to-text model
+# Load the speech-to-text model
 s2t = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
 
 # Function to extract structured information using question answering
@@ -40,23 +40,29 @@ def extract_structured_info(note):
     return answers
 
 def process_audio(audio):
-    if audio is None:
-        return "No audio provided", "N/A", "N/A", "N/A", "N/A"
+    try:
+        if audio is None:
+            return "No audio provided", "N/A", "N/A", "N/A", "N/A"
 
-    # Transcribe audio to text
-    transcription_result = s2t(audio)
-    transcription = transcription_result.get("text", "")
+        # Transcribe audio to text
+        transcription_result = s2t(audio)
+        transcription = transcription_result.get("text", "")
 
-    # Extract structured information
-    structured_info = extract_structured_info(transcription)
+        # Extract structured information
+        structured_info = extract_structured_info(transcription)
 
-    return (
-        transcription, 
-        structured_info["Patient Name"], 
-        structured_info["Age"], 
-        structured_info["Medical History"], 
-        structured_info["Physical Examination"]
-    )
+        return (
+            transcription, 
+            structured_info["Patient Name"], 
+            structured_info["Age"], 
+            structured_info["Medical History"], 
+            structured_info["Physical Examination"]
+        )
+
+    except Exception as e:
+        # Capture any errors and display them in output fields
+        error_message = f"Error: {str(e)}"
+        return error_message, error_message, error_message, error_message, error_message
 
 # Set up Gradio Interface with structured outputs
 iface = gr.Interface(
@@ -71,7 +77,6 @@ iface = gr.Interface(
         gr.Textbox(label="Medical History"),
         gr.Textbox(label="Physical Examination"),
     ],
-    live=False  # Disable live updates for faster response
+    live=True  # Automatically triggers on new input
 )
-
 iface.launch()