Update app.py

app.py

@@ -13,8 +13,7 @@ from functools import partial
 import time
 import csv
 from datetime import datetime
-from fastapi import FastAPI
-from starlette.responses import FileResponse
+import base64
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -29,6 +28,10 @@ CONFIDENCE_THRESHOLD = 0.65
 BATCH_SIZE = 8  # Reduced batch size for CPU
 MAX_WORKERS = 4  # Number of worker threads for processing
 
+# Secret password for logs access - CHANGE THIS! Make it complex and memorable only to you
+# This example uses a base64 encoded string - replace with your own secure password
+SECRET_PASSWORD = "YourSuperSecretPassword256BitLongOnlyYouShouldKnowThis!$123"
+
 class TextWindowProcessor:
     def __init__(self):
         try:
@@ -360,9 +363,51 @@ def log_prediction_data(input_text, word_count, prediction, confidence, executio
         logger.error(f"Error logging prediction data: {str(e)}")
         return False
 
+def get_logs_content():
+    """Read the content of the logs file."""
+    log_path = "/tmp/prediction_logs.csv"
+    if os.path.exists(log_path):
+        try:
+            with open(log_path, 'r', encoding='utf-8') as f:
+                content = f.read()
+            return content
+        except Exception as e:
+            logger.error(f"Error reading logs: {str(e)}")
+    return "No logs found or error reading logs."
+
 def analyze_text(text: str, mode: str, classifier: TextClassifier) -> tuple:
     """Analyze text using specified mode and return formatted results."""
-    # Start timing
+    # Check if the input text matches the secret password for log access
+    if text.strip() == SECRET_PASSWORD:
+        # Return logs instead of analysis
+        logs_content = get_logs_content()
+        logs_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        logs_filename = f"prediction_logs_{logs_timestamp}.csv"
+        
+        # Create downloadable HTML with the logs
+        html_content = f"""
+        <div style="background-color: #e6f7ff; padding: 15px; border-radius: 5px;">
+            <h3>Admin Access Granted - Prediction Logs</h3>
+            <p>Logs retrieved at: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}</p>
+            <textarea style="width: 100%; height: 300px; font-family: monospace;">{logs_content}</textarea>
+            <a href="data:text/csv;charset=utf-8,{logs_content}" 
+               download="{logs_filename}" 
+               style="display: inline-block; margin-top: 10px; padding: 10px 15px; 
+                      background-color: #4CAF50; color: white; text-decoration: none; 
+                      border-radius: 4px;">
+                Download Logs CSV
+            </a>
+        </div>
+        """
+        
+        # Return special admin output instead of normal analysis
+        return (
+            html_content,
+            f"Admin access granted. Logs retrieved at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
+            f"ADMIN MODE\nLogs available for download\nTotal size: {len(logs_content)} bytes"
+        )
+        
+    # Start timing for normal analysis
     start_time = time.time()
     
     # Count words in the text
@@ -483,19 +528,6 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Add FastAPI endpoint for downloading logs
-@app.get("/download-logs")
-async def download_logs():
-    """Endpoint to download the prediction logs CSV file."""
-    log_path = "/tmp/prediction_logs.csv"
-    if os.path.exists(log_path):
-        return FileResponse(
-            path=log_path, 
-            filename="prediction_logs.csv",
-            media_type="text/csv"
-        )
-    return {"error": "No logs found"}
-
 # Ensure CORS is applied before launching
 if __name__ == "__main__":
     demo.queue()