added: gradio app file and tested on local

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----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=
+-----END CERTIFICATE-----

app.py

@@ -0,0 +1,115 @@
+import gradio as gr
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from pathlib import Path
+from torchvision import transforms
+from src.models.catdog_model_resnet import ResnetClassifier
+from src.utils.aws_s3_services import S3Handler
+from src.utils.logging_utils import setup_logger
+from loguru import logger
+import rootutils
+
+# Load environment variables and configure logger
+setup_logger(Path("./logs") / "gradio_app.log")
+# Setup root directory
+root = rootutils.setup_root(__file__, indicator=".project-root")
+
+
+class ImageClassifier:
+    def __init__(self, cfg):
+        self.cfg = cfg
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.classes = cfg.labels
+
+        # Download and load model from S3
+        logger.info("Downloading model from S3...")
+        s3_handler = S3Handler(bucket_name="deep-bucket-s3")
+        s3_handler.download_folder("checkpoints", "checkpoints")
+
+        logger.info("Loading model checkpoint...")
+        self.model = ResnetClassifier.load_from_checkpoint(
+            checkpoint_path=cfg.ckpt_path
+        )
+        self.model = self.model.to(self.device)
+        self.model.eval()
+
+        # Image transform
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize((cfg.data.image_size, cfg.data.image_size)),
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                ),
+            ]
+        )
+
+    def predict(self, image):
+        if image is None:
+            return "No image provided.", None
+
+        # Preprocess the image
+        logger.info("Processing input image...")
+        img_tensor = self.transform(image).unsqueeze(0).to(self.device)
+
+        # Inference
+        with torch.no_grad():
+            output = self.model(img_tensor)
+            probabilities = F.softmax(output, dim=1)
+            predicted_class_idx = torch.argmax(probabilities, dim=1).item()
+            confidence = probabilities[0][predicted_class_idx].item()
+
+        predicted_label = self.classes[predicted_class_idx]
+        logger.info(f"Prediction: {predicted_label} (Confidence: {confidence:.2f})")
+        return predicted_label, confidence
+
+
+def create_gradio_app(cfg):
+    classifier = ImageClassifier(cfg)
+
+    def classify_image(image):
+        """Gradio interface function."""
+        predicted_label, confidence = classifier.predict(image)
+        if predicted_label:
+            return f"Predicted: {predicted_label} (Confidence: {confidence:.2f})"
+        return "Error during prediction."
+
+    # Create Gradio interface
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+            # Cat vs Dog Classifier
+            Upload an image of a cat or a dog to classify it with confidence.
+            """
+        )
+
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(
+                    label="Input Image", type="pil", image_mode="RGB"
+                )
+                predict_button = gr.Button("Classify")
+            with gr.Column():
+                output_text = gr.Textbox(label="Prediction")
+
+        # Define interaction
+        predict_button.click(
+            fn=classify_image, inputs=[input_image], outputs=[output_text]
+        )
+
+    return demo
+
+
+# Hydra config wrapper for launching Gradio app
+if __name__ == "__main__":
+    import hydra
+    from omegaconf import DictConfig
+
+    @hydra.main(config_path="configs", config_name="infer", version_base="1.3")
+    def main(cfg: DictConfig):
+        logger.info("Launching Gradio App...")
+        demo = create_gradio_app(cfg)
+        demo.launch(share=True, server_name="0.0.0.0", server_port=7860)
+
+    main()

src/utils/aws_s3_services.py

@@ -51,14 +51,24 @@ class S3Handler:
             s3_folder (str): Source folder in S3.
             dest_folder (str): Local destination folder path.
         """
-        dest_folder = Path(dest_folder)
+        dest_folder = Path(dest_folder).resolve()
         paginator = self.s3.get_paginator("list_objects_v2")
 
         for page in paginator.paginate(Bucket=self.bucket_name, Prefix=s3_folder):
             for obj in page.get("Contents", []):
                 s3_path = obj["Key"]
-                local_path = dest_folder / Path(s3_path).relative_to(s3_folder)
+                # Skip folder itself if returned by S3
+                if s3_path.endswith("/"):
+                    continue
+
+                # Compute relative path and local destination
+                relative_path = Path(s3_path[len(s3_folder) :].lstrip("/"))
+                local_path = dest_folder / relative_path
+
+                # Create necessary local directories
                 local_path.parent.mkdir(parents=True, exist_ok=True)
+
+                # Download file
                 self.s3.download_file(self.bucket_name, s3_path, str(local_path))
                 print(f"Downloaded: {s3_path} to {local_path}")
 
@@ -71,8 +81,8 @@ if __name__ == "__main__":
     # Upload specific files
     s3_handler.upload_folder(
         "checkpoints",
-        "checkpoints_test",
+        "checkpoints",
     )
 
     # Download example
-    s3_handler.download_folder("checkpoints_test", "checkpoints")
+    s3_handler.download_folder("checkpoints", "checkpoints")