Add enhanced radiology teaching application with logging and version tracking

app.py

@@ -5,10 +5,25 @@ from openai import OpenAI
 from PIL import Image
 import io
 import base64
+import logging
 
-# Load only 10 rows from the dataset
-dataset = load_dataset("itsanmolgupta/mimic-cxr-dataset", split="train").select(range(10))
-df = pd.DataFrame(dataset)
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# App version
+APP_VERSION = "1.0.0"
+logger.info(f"Starting Radiology Teaching App v{APP_VERSION}")
+
+try:
+    # Load only 10 rows from the dataset
+    logger.info("Loading MIMIC-CXR dataset...")
+    dataset = load_dataset("itsanmolgupta/mimic-cxr-dataset", split="train").select(range(10))
+    df = pd.DataFrame(dataset)
+    logger.info(f"Successfully loaded {len(df)} cases")
+except Exception as e:
+    logger.error(f"Error loading dataset: {str(e)}")
+    raise
 
 def encode_image_to_base64(image_bytes):
     return base64.b64encode(image_bytes).decode('utf-8')
@@ -19,6 +34,7 @@ def analyze_report(user_findings, ground_truth_findings, ground_truth_impression
     
     try:
         client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
+        logger.info("Analyzing report with DeepSeek...")
         
         prompt = f"""You are an expert radiologist reviewing a trainee's chest X-ray report. 
         
@@ -49,18 +65,24 @@ def analyze_report(user_findings, ground_truth_findings, ground_truth_impression
         
         return response.choices[0].message.content
     except Exception as e:
+        logger.error(f"Error in report analysis: {str(e)}")
         return f"Error analyzing report: {str(e)}"
 
 def load_random_case(hide_ground_truth):
-    # Randomly select a case from our dataset
-    random_case = df.sample(n=1).iloc[0]
-    
-    # Get the image, findings, and impression
-    image = random_case['image']
-    findings = "" if hide_ground_truth else random_case['findings']
-    impression = "" if hide_ground_truth else random_case['impression']
-    
-    return image, findings, impression
+    try:
+        # Randomly select a case from our dataset
+        random_case = df.sample(n=1).iloc[0]
+        logger.info("Loading random case...")
+        
+        # Get the image, findings, and impression
+        image = random_case['image']
+        findings = "" if hide_ground_truth else random_case['findings']
+        impression = "" if hide_ground_truth else random_case['impression']
+        
+        return image, findings, impression
+    except Exception as e:
+        logger.error(f"Error loading random case: {str(e)}")
+        return None, "Error loading case", "Error loading case"
 
 def process_case(image, user_findings, hide_ground_truth, api_key, current_findings="", current_impression=""):
     if hide_ground_truth:
@@ -71,7 +93,7 @@ def process_case(image, user_findings, hide_ground_truth, api_key, current_findi
 
 # Create the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Radiology Report Training System")
+    gr.Markdown(f"# Radiology Report Training System v{APP_VERSION}")
     gr.Markdown("### Practice your chest X-ray reading and reporting skills")
     
     with gr.Row():
@@ -118,4 +140,5 @@ with gr.Blocks() as demo:
         outputs=[ground_truth_findings, ground_truth_impression, analysis_output]
     )
 
+logger.info("Starting Gradio interface...")
 demo.launch()