Update app.py

app.py

@@ -1,364 +1,172 @@
-# --- Imports ---
 import os
 import sys
-import cv2
-import torch
-# Delay Gradio import until after installation
-# import gradio as gr
-import numpy as np
-from PIL import Image, ImageOps # <--- IMPORT ImageOps
-import io
-import base64
-import traceback
-# Delay spaces import until after installation
-# import spaces
 
-# --- Dependency Management ---
-# Force install specific compatible versions
-print("Installing specific Gradio and huggingface-hub versions...")
-os.system("pip uninstall -y gradio gradio-client huggingface-hub") # Uninstall all first
-os.system("pip install gradio==4.13.0 gradio-client==0.8.0") # Install specific Gradio
-os.system("pip install huggingface-hub==0.19.4") # Install older huggingface-hub
-print("Dependency installation complete.")
+# Force install a Gradio version that is compatible with Spaces
+os.system("pip uninstall -y gradio gradio-client")
+os.system("pip install gradio==4.13.0")  # This version should be compatible with Spaces
 
-# Now import Gradio and spaces
+import cv2
 import gradio as gr
+import torch
+from basicsr.archs.srvgg_arch import SRVGGNetCompact
+from gfpgan.utils import GFPGANer
+from realesrgan.utils import RealESRGANer
 import spaces
-# from huggingface_hub import HfFolder # Example import from hub if needed
 
-# Check installed versions (optional but helpful for debugging)
+# download weights - using try/except to handle potential errors
 try:
-    import pkg_resources
-    gradio_version = pkg_resources.get_distribution("gradio").version
-    hub_version = pkg_resources.get_distribution("huggingface-hub").version
-    print(f"Using Gradio version: {gradio_version}")
-    print(f"Using huggingface-hub version: {hub_version}")
+    if not os.path.exists('realesr-general-x4v3.pth'):
+        os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
+    if not os.path.exists('GFPGANv1.2.pth'):
+        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
+    if not os.path.exists('GFPGANv1.3.pth'):
+        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
+    if not os.path.exists('GFPGANv1.4.pth'):
+        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
+    if not os.path.exists('RestoreFormer.pth'):
+        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
+    if not os.path.exists('CodeFormer.pth'):
+        os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
 except Exception as e:
-    print(f"Could not check package versions: {e}")
-
+    print(f"Error downloading model weights: {e}")
 
-# Import model-specific libraries AFTER installing dependencies
+# Download sample images - using try/except to handle potential errors
 try:
-    from basicsr.archs.srvgg_arch import SRVGGNetCompact
-    from gfpgan.utils import GFPGANer
-    from realesrgan.utils import RealESRGANer
-    print("Successfully imported model libraries.")
-except ImportError as e:
-    print(f"Error importing model libraries: {e}")
-    print("Please ensure basicsr, gfpgan, realesrgan are installed or in requirements.txt")
-    sys.exit(1)
-
-# --- Constants ---
-OUTPUT_DIR = 'output' # This might not be strictly needed if not saving via gr.File
-os.makedirs(OUTPUT_DIR, exist_ok=True)
-
-# --- Model Weight Downloads ---
-MODEL_FILES = {
-    'realesr-general-x4v3.pth': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth',
-    'GFPGANv1.2.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth',
-    'GFPGANv1.3.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
-    'GFPGANv1.4.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
-    'RestoreFormer.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth',
-}
-print("Downloading model weights...")
-for filename, url in MODEL_FILES.items():
-    try:
-        if not os.path.exists(filename):
-            print(f"Downloading {filename}...")
-            os.system(f"wget -q {url} -P .") # Use -q for quiet
-    except Exception as e:
-        print(f"Error downloading {filename}: {e}")
-if not os.path.exists('realesr-general-x4v3.pth'):
-    print("FATAL: RealESRGAN model (realesr-general-x4v3.pth) not found after download attempt. Cannot proceed.")
-    sys.exit(1)
-print("Model weight download check complete.")
-
-# --- Sample Image Downloads ---
-SAMPLE_IMAGES = {
-    'lincoln.jpg': 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
-    'AI-generate.jpg': 'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
-    'Blake_Lively.jpg': 'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
-    '10045.png': 'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png'
-}
-print("Downloading sample images...")
-for filename, url in SAMPLE_IMAGES.items():
-    try:
-        if not os.path.exists(filename):
-             torch.hub.download_url_to_file(url, filename, progress=False)
-    except Exception as e:
-        print(f"Warning: Error downloading sample image {filename}: {e}")
-print("Sample image download check complete.")
-
+    torch.hub.download_url_to_file(
+        'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
+        'lincoln.jpg')
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
+        'AI-generate.jpg')
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
+        'Blake_Lively.jpg')
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
+        '10045.png')
+except Exception as e:
+    print(f"Error downloading sample images: {e}")
 
-# --- Model Initialization (Background Enhancer) ---
-upsampler = None
+# background enhancer with RealESRGAN
 try:
-    print("Initializing RealESRGAN upsampler...")
     model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
     model_path = 'realesr-general-x4v3.pth'
-    half = torch.cuda.is_available()
-    print(f"CUDA available: {torch.cuda.is_available()}, Using half precision: {half}")
+    half = True if torch.cuda.is_available() else False
     upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
-    print("Successfully created RealESRGAN upsampler.")
+    print("Successfully created upsampler")
 except Exception as e:
-    print(f"Error creating RealESRGAN upsampler: {e}")
-    print(traceback.format_exc())
-    print("Warning: GFPGAN will run without background enhancement.")
-
-
-# --- Inference Function (Handles API, PIL, Base64, EXIF) ---
-@spaces.GPU(duration=90) # Keep ZeroGPU decorator
-# --- MODIFIED Function Signature: Accepts filepath instead of PIL Image ---
-def inference(input_image_filepath, version, scale):
-    """
-    Processes an input image file using GFPGAN, handling EXIF and aspect ratio.
-
-    Args:
-        input_image_filepath (str): Path to the temporary input image file provided by Gradio.
-        version (str): GFPGAN model version ('v1.2', 'v1.3', 'v1.4', 'RestoreFormer').
-        scale (float): Rescaling factor for the final output relative to original.
-
-    Returns:
-        tuple: (PIL.Image.Image | None, str | None)
-               - Output PIL image (RGB) or None on error.
-               - Base64 encoded output image string (data URI) or an error message string.
-    """
-    # --- ADDED: Load image from filepath ---
-    input_pil_image = None
-    try:
-        print(f"Loading image from filepath: {input_image_filepath}")
-        if not input_image_filepath or not isinstance(input_image_filepath, str) or not os.path.exists(input_image_filepath):
-             error_msg = f"Error: Input image filepath is invalid or file does not exist: '{input_image_filepath}'"
-             print(error_msg)
-             # Return None for Image output, error message for Textbox output
-             return None, error_msg
-        input_pil_image = Image.open(input_image_filepath)
-        print(f"Successfully loaded image. Initial mode: {input_pil_image.mode}, size: {input_pil_image.size}")
-    except Exception as load_err:
-        error_msg = f"Error loading image from filepath {input_image_filepath}: {load_err}"
-        print(error_msg)
-        print(traceback.format_exc())
-        # Return None for Image output, error message for Textbox output
-        return None, error_msg
-    # --- End Added Image Loading ---
-
-    # Check if loading failed (redundant due to try/except, but safe)
-    if input_pil_image is None:
-        print("Error: No input image could be loaded.")
-        return None, "Error: Failed to load input image."
-
-    print(f"Processing image with GFPGAN version: {version}, scale: {scale}")
-
-    # --- Handle EXIF Orientation ---
-    original_size_before_exif = input_pil_image.size
-    try:
-        input_pil_image = ImageOps.exif_transpose(input_pil_image)
-        if input_pil_image.size != original_size_before_exif:
-            print(f"Image size changed by EXIF transpose: {original_size_before_exif} -> {input_pil_image.size}")
-    except Exception as exif_err:
-        print(f"Warning: Could not apply EXIF transpose: {exif_err}")
-    # -----------------------------
+    print(f"Error creating model or upsampler: {e}")
+    upsampler = None
 
-    w_orig, h_orig = input_pil_image.size
-    print(f"Input size for processing (WxH): {w_orig}x{h_orig}")
+os.makedirs('output', exist_ok=True)
 
-    # Convert PIL Image to OpenCV format (BGR numpy array)
+# Apply the spaces.GPU decorator to enable ZeroGPU allocation
+@spaces.GPU(duration=90)
+def inference(img, version, scale):
     try:
-        img_mode = input_pil_image.mode
-        if img_mode == 'RGBA':
-             input_pil_image = input_pil_image.convert('RGB')
-        elif img_mode != 'RGB':
-             print(f"Converting input image from {img_mode} to RGB")
-             input_pil_image = input_pil_image.convert('RGB')
-        img_bgr = np.array(input_pil_image)[:, :, ::-1].copy()
-    except Exception as conversion_err:
-        error_msg = f"Error converting PIL image to OpenCV format: {conversion_err}"
-        print(error_msg)
-        print(traceback.format_exc())
-        return None, error_msg # Return None for Image, error string for Textbox
-
-    # --- Start GFPGAN Processing ---
-    try:
-        h, w = img_bgr.shape[0:2]
-        if h > 4000 or w > 4000:
-            print(f'Warning: Image size ({w}x{h}) is very large, processing might be slow or fail.')
-
-        model_map = {
-            'v1.2': 'GFPGANv1.2.pth', 'v1.3': 'GFPGANv1.3.pth',
-            'v1.4': 'GFPGANv1.4.pth', 'RestoreFormer': 'RestoreFormer.pth'
-        }
-        arch_map = {
-            'v1.2': 'clean', 'v1.3': 'clean', 'v1.4': 'clean',
-            'RestoreFormer': 'RestoreFormer'
-        }
-
-        if version not in model_map:
-            error_msg = f"Error: Unknown version selected: {version}"
-            print(error_msg)
-            return None, error_msg
-        model_path = model_map[version]
-        arch = arch_map[version]
-        if not os.path.exists(model_path):
-             error_msg = f"Error: Model file not found for version {version}: {model_path}"
-             print(error_msg)
-             return None, error_msg
-
-        current_bg_upsampler = upsampler
-        if not current_bg_upsampler:
-             print("Warning: RealESRGAN upsampler not available. Background enhancement disabled.")
-
-        face_enhancer = GFPGANer(
-            model_path=model_path, upscale=2, arch=arch,
-            channel_multiplier=2, bg_upsampler=current_bg_upsampler
-        )
-
-        print(f"Running GFPGAN enhancement with {version}...")
-        _, _, output_bgr = face_enhancer.enhance(
-            img_bgr, has_aligned=False, only_center_face=False, paste_back=True
-        )
-        if output_bgr is None:
-             error_msg = "Error: GFPGAN enhancement returned None."
-             print(error_msg)
-             return None, error_msg
-        print(f"Enhancement complete. Intermediate output shape (HxWxC BGR): {output_bgr.shape}")
-
-        # --- Post-processing (Resizing) ---
-        target_scale_factor = float(scale)
-        h_gfpgan, w_gfpgan = output_bgr.shape[0:2]
-        target_w = int(w_orig * target_scale_factor)
-        target_h = int(h_orig * target_scale_factor)
-
-        if target_w <= 0 or target_h <= 0:
-             print(f"Warning: Invalid target size ({target_w}x{target_h}) calculated from scale {scale}. Using GFPGAN output size {w_gfpgan}x{h_gfpgan}.")
-             target_w, target_h = w_gfpgan, h_gfpgan
-
-        if abs(target_w - w_gfpgan) > 2 or abs(target_h - h_gfpgan) > 2:
-             print(f"Resizing GFPGAN output ({w_gfpgan}x{h_gfpgan}) to target ({target_w}x{target_h}) based on scale {target_scale_factor}...")
-             interpolation = cv2.INTER_LANCZOS4 if (target_w * target_h) > (w_gfpgan * h_gfpgan) else cv2.INTER_AREA
-             try:
-                 output_bgr = cv2.resize(output_bgr, (target_w, target_h), interpolation=interpolation)
-             except cv2.error as resize_err:
-                 # --- MODIFIED Resize Error Handling ---
-                 error_msg = f"Error during OpenCV resize: {resize_err}. Returning image before final resize attempt."
-                 print(error_msg)
-                 output_pil = Image.fromarray(cv2.cvtColor(output_bgr, cv2.COLOR_BGR2RGB))
-                 # Still try to encode this fallback image
-                 base64_output = None
-                 try:
-                     buffered = io.BytesIO()
-                     output_pil.save(buffered, format="WEBP", quality=85)
-                     img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-                     base64_output = f"data:image/webp;base64,{img_str}"
-                 except Exception as enc_err:
-                     print(f"Error encoding fallback image: {enc_err}")
-                     error_msg += f" | Encoding Error: {enc_err}" # Append encoding error
-                 # Return fallback image and combined error message
-                 return output_pil, base64_output if base64_output else error_msg
-                 # --- End Modified Resize Error Handling ---
-
-        # --- Convert final result back to PIL (RGB) ---
-        output_pil = Image.fromarray(cv2.cvtColor(output_bgr, cv2.COLOR_BGR2RGB))
-        print(f"Final output image size (WxH PIL): {output_pil.size}")
-
-        # --- Encode final PIL image to Base64 for API ---
-        # This replaces the need for gr.File saving/output
-        base64_output = None
-        try:
-            buffered = io.BytesIO()
-            output_pil.save(buffered, format="WEBP", quality=90)
-            img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-            base64_output = f"data:image/webp;base64,{img_str}"
-        except Exception as enc_err:
-            error_msg = f"Error encoding final image to base64: {enc_err}"
-            print(error_msg)
-            print(traceback.format_exc())
-            # Return the PIL image anyway, but with an error message
-            return output_pil, error_msg
-
-        # --- MODIFIED RETURN: Return PIL image and base64 string ---
-        # Remove save_path which is no longer needed for outputs
-        # Return the base64 string for the Textbox output (or success message)
-        success_msg = f"Success! Output size: {output_pil.size[0]}x{output_pil.size[1]}"
-        return output_pil, base64_output if base64_output else success_msg
-
+        print(f"Processing image: {img}, version: {version}, scale: {scale}")
+        if scale > 4:
+            scale = 4  # avoid too large scale value
+        
+        extension = os.path.splitext(os.path.basename(str(img)))[1]
+        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
+        if img is None:
+            print(f"Failed to read image: {img}")
+            return None, None
+            
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        elif len(img.shape) == 2:  # for gray inputs
+            img_mode = None
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        else:
+            img_mode = None
+
+        h, w = img.shape[0:2]
+        if h > 3500 or w > 3500:
+            print('too large size')
+            return None, None
+        
+        if h < 300:
+            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
+
+        if upsampler is None:
+            print("Upsampler not initialized")
+            return None, None
+
+        if version == 'v1.2':
+            face_enhancer = GFPGANer(
+            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'v1.3':
+            face_enhancer = GFPGANer(
+            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'v1.4':
+            face_enhancer = GFPGANer(
+            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'RestoreFormer':
+            face_enhancer = GFPGANer(
+            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
+        else:
+            print(f"Unknown version: {version}")
+            return None, None
+
+        print(f"Using {version} for enhancement")
+        _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+
+        if scale != 2:
+            interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
+            h, w = img.shape[0:2]
+            output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
+            
+        if img_mode == 'RGBA':  # RGBA images should be saved in png format
+            extension = 'png'
+        else:
+            extension = 'jpg'
+        save_path = f'output/out.{extension}'
+        
+        cv2.imwrite(save_path, output)
+        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+        return output, save_path
+    
     except Exception as error:
-        # --- MODIFIED Main Exception Handling ---
-        error_msg = f"Error during GFPGAN processing: {error}"
-        print(error_msg)
+        import traceback
+        print(f"Error in inference: {error}")
         print(traceback.format_exc())
-        # Return placeholder image (or None) and error message string
-        error_img = None
-        try:
-             error_img = Image.new('RGB', (100, 50), color = 'red')
-        except Exception: pass # Ignore if placeholder fails
-        return error_img, error_msg # Return placeholder/None and error string
-        # --- End Modified Main Exception Handling ---
-
+        return None, None
 
-# --- Gradio Interface Definition ---
-title = "GFPGAN: Practical Face Restoration"
-description = """Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.
-<br>Restore your <b>old photos</b> or improve <b>AI-generated faces</b>. Upload an image to start.
-<br>If helpful, please ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Original Github Repo</a>.
-<br>API endpoint available at `/predict`. Expects image filepath, version string, scale number. Returns Image and Textbox data.
-"""
-article = "Questions? Contact the original creators (see GFPGAN repo)."
 
-print("Creating Gradio interface...")
-try:
-    # --- MODIFIED INPUTS ---
-    # Changed gr.Image type to 'filepath'
-    inputs = [
-        gr.Image(type="filepath", label="Input Image", sources=["upload", "clipboard"]), # <-- TYPE CHANGED
-        gr.Radio(
-            ['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'],
-            type="value", value='v1.4', label='GFPGAN Version',
-            info="v1.4 recommended. RestoreFormer for diverse poses."
-        ),
-        gr.Number(
-            label="Rescaling Factor", value=2,
-            info="Final output size multiplier relative to original input size (e.g., 2 = 2x original WxH)."
-        ),
-    ]
-
-    # --- MODIFIED OUTPUTS ---
-    # Removed gr.File, kept gr.Image and gr.Textbox
-    # Made Textbox visible for easier debugging
-    outputs = [
-        gr.Image(type="pil", label="Output Image"),
-        # gr.File(label="Download Output Image (Server Path)"), # <-- REMOVED
-        gr.Textbox(label="Output Info / Base64 Data", interactive=False, visible=True) # <-- KEPT (made visible)
-    ]
+title = "GFPGAN: Practical Face Restoration Algorithm"
+description = "Gradio demo Fix for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>It can be used to restore your <b>old photos</b> or improve <b>AI-generated faces</b>.<br>To use it, simply upload your image.<br>If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/Nick088Official/GFPGAN-Fix' target='_blank'>Github Repo</a> and recommend it to your friends 😊"
 
-    # Define examples using file paths (Gradio handles loading them)
-    # These should still work even with type="filepath" for the input component
-    examples = [
-        ['AI-generate.jpg', 'v1.4', 2],
-        ['lincoln.jpg', 'v1.4', 2],
-        ['Blake_Lively.jpg', 'v1.4', 2],
-        ['10045.png', 'v1.4', 2]
-    ]
+# Simplified article text to avoid URL parsing issues
+article = "If you have any question, please email [email protected] or [email protected] (original creators)."
 
-    # --- Gradio Interface Instantiation ---
-    # Ensure fn=inference points to the modified function
+# Create the interface with more robust error handling
+try:
+    print("Creating Gradio interface...")
     demo = gr.Interface(
-        fn=inference, # Should now accept filepath and return 2 items
-        inputs=inputs, # Use modified inputs list
-        outputs=outputs, # Use modified outputs list
+        fn=inference,
+        inputs=[
+            gr.Image(type="filepath", label="Input"),
+            gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
+            gr.Number(label="Rescaling factor", value=2),
+        ],
+        outputs=[
+            gr.Image(type="numpy", label="Output (The whole image)"),
+            gr.File(label="Download the output image")
+        ],
         title=title,
         description=description,
         article=article,
-        examples=examples,
-        cache_examples=False, # Caching might be complex with filepaths vs PIL
-        allow_flagging='never'
+        examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2],
+                  ['10045.png', 'v1.4', 2]]
     )
-
-    # Launch the interface
+    
+    # Launch with the simplest possible configuration
     print("Launching Gradio interface...")
-    demo.queue().launch(server_name="0.0.0.0", share=False)
-    print("Gradio app launched successfully and should be accessible.")
-
+    demo.launch(server_name="0.0.0.0")
+    
 except Exception as e:
-    print(f"Error setting up or launching Gradio interface: {e}")
-    print(traceback.format_exc())
-    sys.exit(1)
+    import traceback
+    print(f"Error setting up Gradio interface: {e}")
+    print(traceback.format_exc())