First of many

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+EfficientNet_b2.pth filter=lfs diff=lfs merge=lfs -text
+examples/person10_virus_35.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/person100_bacteria_475.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/person101_bacteria_483.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/person103_bacteria_489.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/person1608_virus_2786.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/IM-0001-0001.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/IM-0011-0001-0001.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/IM-0016-0001.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/person1_virus_6.jpeg filter=lfs diff=lfs merge=lfs -text

.gitignore

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+venv/
+__pycache__/
+flagged/

EfficientNet_b0.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e1245266f52ce06f3019f18832199587f9007b848158f6b8f9aea08d469eccaf
+size 16346498

EfficientNet_b2.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f47b49b1538f5c24e37f34b1720aece156cfcb35153d74f0cd8a2af5cdb1269
+size 31278138

LICENSE.txt

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+MIT License
+
+Copyright (c) 2024 Timothy Karani
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

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+### 1. Imports and class setup
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb0_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+with open("class_names.txt", "r") as f:
+    class_names = [class_name.strip() for class_name in f.readlines()]
+
+## 2. Model and transforms preparation
+effnetb0, effnetb0_transforms = create_effnetb0_model(num_classes=3)
+
+# Load saved weights
+effnetb0.load_state_dict(
+    torch.load(f="EfficientNet_b0.pth", map_location=torch.device("cpu"))
+)
+
+
+### 3. Predict function
+def predict(img) -> Tuple[Dict, float]:
+    # Start a timer
+    start_time = timer()
+
+    # Transform the input image for use with EffNetB0
+    img = effnetb0_transforms(img).unsqueeze(
+        0
+    )  # unsqueeze = add batch dimension on 0th index
+
+    # Put model into eval mode, make prediction
+    effnetb0.eval()
+    with torch.inference_mode():
+        # Pass transformed image through the model and turn the prediction logits into probaiblities
+        pred_probs = torch.softmax(effnetb0(img), dim=1)
+
+    # Create a prediction label and prediction probability dictionary
+    pred_labels_and_probs = {
+        class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
+    }
+
+    # Calculate pred time
+    end_time = timer()
+    pred_time = round(end_time - start_time, 4)
+
+    # Return pred dict and pred time
+    return pred_labels_and_probs, pred_time
+
+
+### 4. Gradio app ###
+# Create title, description and article
+title = "EffNet Pneumonia, by Timothy Karani"
+description = "An EfficientNetB0 model for multiclass pneumonia detection"
+article = "AN EFFICIENT DEEP LEARNING APPROACH FOR MULTICLASS PNEUMONIA DETECTION IN CHEST X-RAY IMAGES."
+
+# Create example list
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(
+    fn=predict,  # maps inputs to outputs
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=3, label="Predictions"),
+        gr.Number(label="Prediction time (s)"),
+    ],
+    examples=example_list,
+    title=title,
+    description=description,
+    article=article,
+)
+
+# Launch the demo!
+demo.launch()

class_names.txt

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+NORMAL
+VIRAL
+BACTERIAL

examples/.gitattributes

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+examples/*.jpeg filter=lfs diff=lfs merge=lfs -text

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+
+def create_effnetb0_model(num_classes: int = 3, seed: int = 42):
+    """Creates an EfficientNetB0 Model and Transforms"""
+    # 1. Setup Weights
+    weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT
+
+    # 2. Get transforms
+    transforms = weights.transforms()
+
+    # 3. Setup pretrained model
+    model = torchvision.models.efficientnet_b0(weights=weights)
+
+    # 4 Freeze all layers
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # 5. Change classifier head with random seed for reproducability
+    torch.manual_seed(seed)
+
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.2, inplace=True),
+        nn.Linear(in_features=1280, out_features=num_classes, bias=True),
+    )
+
+    return model, transforms

requirements.txt

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+gradio==4.37.2
+torch
+torchvision
+numpy<2