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Dockerfile

@@ -0,0 +1,18 @@
+FROM public.ecr.aws/docker/library/python:3.11.10-slim
+
+# Install AWS Lambda Web Adapter
+COPY --from=public.ecr.aws/awsguru/aws-lambda-adapter:0.8.4 /lambda-adapter /opt/extensions/lambda-adapter
+
+WORKDIR /var/task
+
+# Copy and install requirements
+COPY requirements.txt ./
+RUN pip install -r requirements.txt
+
+# Copy application code and model
+COPY app.py ./
+COPY best_model.pt ./
+COPY examples/ ./examples/
+
+# Set command
+CMD ["python3", "app.py"] 

app.py

@@ -1,4 +1,5 @@
 import os
+from numpy.lib.type_check import imag
 import torch
 import lightning as pl
 import gradio as gr
@@ -6,11 +7,13 @@ from PIL import Image
 from torchvision import transforms
 from timeit import default_timer as timer
 from torch.nn import functional as F
+from gradio.flagging import SimpleCSVLogger
 
-torch.set_float32_matmul_precision('medium')
-device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+torch.set_float32_matmul_precision("medium")
+# device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+device = torch.device("cpu") 
 torch.set_default_device(device=device)
-torch.autocast(enabled=True, dtype='float16', device_type='cuda')
+torch.autocast(enabled=True, dtype="float16", device_type="cuda")
 
 pl.seed_everything(123, workers=True)
 
@@ -34,7 +37,7 @@ class_labels = [
 
 
 # Model
-model = torch.jit.load('best_model.pt', map_location=device).to(device)
+model:torch.nn.Module = torch.jit.load("best_model.pt", map_location=device).to(device)
 
 
 @torch.no_grad()
@@ -53,27 +56,29 @@ def predict_fn(img: Image):
         predicted_label = class_labels[y_pred]
         # print(confidence,predicted_label)
         pred_time = round(timer() - start_time, 5)
-        res = {f'Title: {predicted_label}': confidence}
+        res = {f"Title: {predicted_label}": confidence}
         return (res, pred_time)
     except Exception as e:
-        print(f'error:: {e}')
-        gr.Error('An error occured 💥!', duration=5)
-        return ({'Title ☠️': 0.0}, 0.0)
+        print(f"error:: {e}")
+        gr.Error("An error occured 💥!", duration=5)
+        return ({"Title ☠️": 0.0}, 0.0)
 
 
 gr.Interface(
     fn=predict_fn,
-    inputs=gr.Image(type='pil'),
+    inputs=gr.Image(type="pil"),
     outputs=[
-        gr.Label(num_top_classes=1, label='Predictions'),  # what are the outputs?
-        gr.Number(label='Prediction time (s)'),
+        gr.Label(num_top_classes=1, label="Predictions"),  # what are the outputs?
+        gr.Number(label="Prediction time (s)"),
     ],
     examples=[
-        ['examples/' + i]
-        for i in os.listdir(os.path.join(os.path.dirname(__file__), 'examples'))
+        ["examples/" + i]
+        for i in os.listdir(os.path.join(os.path.dirname(__file__), "examples"))
     ],
-    title='Dog Breeds Classifier 🐈',
-    description='CNN-based Architecture for Fast and Accurate DogsBreed Classifier',
-    article='Created by muthukamalan.m ❤️',
+    title="Dog Breeds Classifier 🐈",
+    description="CNN-based Architecture for Fast and Accurate DogsBreed Classifier",
+    article="Created by muthukamalan.m ❤️",
     cache_examples=True,
-).launch(share=False, debug=False)
+    flagging_options=[],
+    flagging_callback=SimpleCSVLogger()
+).launch(share=False, debug=False,server_name="0.0.0.0",server_port=8080,enable_monitoring=None)