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app.py

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+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torch
+
+from model import create_resnet50_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
+### 2. Model and transforms preparation ###
+
+# Create  model
+resnet50, resnet50_transforms = create_resnet50_model(num_classes=36,
+                                                      seed=42)
+
+# Load saved weights
+resnet50.load_state_dict(
+    torch.load(
+        f="AMS.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+
+    img = img.convert('RGB')
+    # Transform the target image using the ResNet50 transforms
+    img = resnet50_transforms(img).unsqueeze(0)
+
+    # Put the ResNet50 model into evaluation mode
+    resnet50.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and obtain the prediction logits
+        pred_logits = resnet50(img)
+
+    # Convert the prediction logits to probabilities using softmax
+    pred_probs = torch.softmax(pred_logits, dim=1)
+
+    # Create a prediction label and prediction probability dictionary for each prediction class
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+
+    # Return the prediction dictionary and prediction time
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+import gradio as gr
+
+# Create title, description and article strings
+title = "AMERICA SIGN LAGNGUAGE"
+description = "An resnet50 feature extractor computer vision model to classify american sign language ."
+#article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=5, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    )
+
+# Launch the demo!
+demo.launch()
+     

model.py

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+import torch
+import torchvision
+import torch.nn as nn
+
+def create_resnet50_model(num_classes: int = 2, seed: int = 42):
+    """Creates a ResNet50 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): Number of classes in the classifier head.
+            Defaults to 2.
+        seed (int, optional): Random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): ResNet50 feature extractor model.
+        transforms (torchvision.transforms): ResNet50 image transforms.
+    """
+    # 1. Create ResNet50 pretrained weights and transforms
+    weights = torchvision.models.resnet50(pretrained=True)
+    transforms = torchvision.transforms.Compose([
+        torchvision.transforms.Resize(256),
+        torchvision.transforms.CenterCrop(224),
+        torchvision.transforms.ToTensor(),
+        torchvision.transforms.Normalize(
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225]
+        )
+    ])
+
+    # 2. Create ResNet50 model with pretrained weights
+    model = torchvision.models.resnet50(pretrained=False)
+
+    # 3. Load the pretrained weights into the model
+    model.load_state_dict(weights.state_dict())
+
+    # 4. Freeze all layers in the base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # 5. Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    num_features = model.fc.in_features
+    model.fc = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=num_features, out_features=num_classes),
+    )
+
+    return model, transforms
+

requirements.txt

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+torch==1.12.0
+torchvision==0.13.0
+gradio==3.1.4