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README.md

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+---
+title: ChestXrayClassification
+emoji: 🌖
+colorFrom: gray
+colorTo: purple
+sdk: gradio
+sdk_version: 3.39.0
+app_file: app.py
+pinned: false
+license: openrail
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

alexnet_pretrained.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9685c58431c076bac73b4d0999c2cb62b4c7cd6f28cb5aa622c16dc83e54d736
+size 9920219

app.py

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+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ["Normal", "Pneumonia"]
+
+### 2. Model and transforms preparation ###
+
+# Create EffNetB2 model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=1,  # len(class_names) would also work
+)
+
+# Load saved weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="alexnet_pretrained.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+
+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()
+
+    # Transform the target image and add a batch dimension
+    img = effnetb2_transforms(img).unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    effnetb2.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.sigmoid(effnetb2(img)).squeeze()
+
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    if (torch.round(pred_probs) == 1):
+        pred_labels_and_probs = {
+            'Normal': 1-pred_probs.item(), 'Pneumonia': pred_probs.item()}
+    else:
+        pred_labels_and_probs = {
+            'Normal': pred_probs.item(), 'Pneumonia': 1-pred_probs.item()}
+
+    # 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
+
+
+example_list = [[f"examples/example{i+1}"] for i in range(3)]
+# Create title, description and article strings
+title = "ChestXray Classification"
+description = "An Alexnet computer vision model to classify images of Xray Chest images as Normal or Pneumonia."
+article = "Created at (https://github.com/azizche/chest_xray_Classification)."
+
+# 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=2, 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,
+                    article=article)
+
+# Launch the demo!
+demo.launch()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes: int = 1,
+                          seed: int = 42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of classes in the classifier head. 
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): EffNetB2 feature extractor model. 
+        transforms (torchvision.transforms): EffNetB2 image transforms.
+    """
+    # Create EffNetB2 pretrained weights, transforms and model
+    weights = torchvision.models.AlexNet_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.alexNet(weights=weights)
+
+    # Freeze all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.2,),
+        nn.Linear(in_features=9216, out_features=1),
+    )
+
+    return model, transforms

requirements.txt

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+torch
+torchvision
+gradio