Update app.py

app.py

@@ -3,7 +3,7 @@ from PIL import Image
 import gradio as gr
 import tempfile
 import os
-import base64
+
 
 # Get Hugging Face Token from environment variables
 # This token is crucial for accessing private spaces
@@ -29,30 +29,21 @@ def call_private_api(pil_img: Image.Image):
         Image.Image: The processed image with detected damages from the private API.
     """
     # Save the user-uploaded image to a temporary JPEG file.
-    # This is necessary to get a file path to read its bytes.
+    # This is necessary because gradio_client expects a file path for image inputs.
     file_path = None # Initialize file_path
     try:
         with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
             pil_img.convert("RGB").save(tmp, "JPEG", quality=95)
             file_path = tmp.name  # Store the path to the temporary file
 
-        # Read the image file as bytes
-        with open(file_path, "rb") as f:
-            image_bytes = f.read()
-
-        # Encode the image bytes to a Base64 string
-        # Gradio typically expects Base64 strings to be prefixed with 'data:image/jpeg;base64,'
-        # for images, or just the Base64 string, depending on the version/component.
-        # Let's try with the prefix first, as it's more explicit.
-        base64_image_string = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode('utf-8')
+        # DEBUG: Confirming type and value of file_path
+        print(f"DEBUG: Type of file_path: {type(file_path)}")
+        print(f"DEBUG: Value of file_path: {file_path}")
 
-        # DEBUG: Confirming type and a snippet of the Base64 string
-        print(f"DEBUG: Type of base64_image_string: {type(base64_image_string)}")
-        print(f"DEBUG: Start of base64_image_string: {base64_image_string[:50]}...")
-
-        # Pass the Base64 string directly as the image input
+        # Pass the file_path string directly as the image input.
+        # Temporarily removed all optional parameters to isolate the image input issue.
         result_path = client.predict(
-            image=base64_image_string, # Pass the Base64 encoded image string
+            image=file_path, # Pass the file path directly as a string
             api_name="/predict",
         )
 
@@ -69,6 +60,7 @@ def call_private_api(pil_img: Image.Image):
         if file_path and os.path.exists(file_path):
             os.remove(file_path)
 
+
             
 
 # 3️⃣ Set up the Gradio interface for the public space