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Isatron_v2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7258b776465706522c96b23ce2d88087f8a7a0ae49f5f1139d83a0a56a1f9e98
+size 275130272

README.md

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 ---
-title: Isatron V2
-emoji: 🏃
-colorFrom: purple
-colorTo: pink
+title: Pneumonia Detection
+emoji: 🏢
+colorFrom: blue
+colorTo: red
 sdk: gradio
-sdk_version: 4.28.3
+sdk_version: 4.25.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from keras.models import load_model
+
+# Load the trained models
+model1 = load_model('./model1.h5')
+# model2 = load_model('./model2.h5')
+
+# Print the loaded models
+print(model1)
+# print(model2)
+
+# Define image size and labels
+img_size1 = 150
+# img_size2 = 224
+labels = ['PNEUMONIA', 'NORMAL']
+
+def load_and_preprocess_image1(img):
+    # Convert Gradio image (PIL Image) to numpy array
+    img = np.array(img)
+    # Convert RGB to grayscale
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    # Resize image to the required input size
+    img = cv2.resize(img, (img_size1, img_size1))
+    # Reshape image for model input
+    img = img.reshape(-1, img_size1, img_size1, 1)
+    # Normalize image
+    img = img / 255.0
+    return img
+
+# def load_and_preprocess_image2(img):
+#     # Convert Gradio image (PIL Image) to numpy array
+#     img = np.array(img)
+#     # Convert RGB to grayscale
+#     img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+#     # Resize image to the required input size
+#     img = cv2.resize(img, (img_size2, img_size2))
+#     # Reshape image for model input
+#     img = img.reshape(-1, img_size2, img_size2, 1)
+#     # Normalize image
+#     img = img / 255.0
+#     return img
+
+def image_classifier1(img):
+    # Preprocess the image
+    img = load_and_preprocess_image1(img)
+    # Perform prediction using model1
+    prediction = model1.predict(img)[0][0]  # Assuming your model outputs a single probability
+    # Return the prediction
+    return {'PNEUMONIA': prediction, 'NORMAL': 1-prediction}  # Invert the prediction for Gradio
+
+# def image_classifier2(img):
+#     # Preprocess the image
+#     img = load_and_preprocess_image2(img)
+#     # Perform prediction using model2
+#     prediction = model2.predict(img)[0][0]  # Assuming your model outputs a single probability
+#     # Return the prediction
+#     return {'PNEUMONIA': 1-prediction, 'NORMAL': prediction}  # Invert the prediction for Gradio
+
+# Create Gradio interfaces for each model
+demo_model1 = gr.Interface(fn=image_classifier1, inputs="image", outputs="label", title="Pneumonia Detection - Model 1")
+# demo_model2 = gr.Interface(fn=image_classifier2, inputs="image", outputs="label", title="Pneumonia Detection - Model 2")
+
+# gr.Parallel(demo_model1, demo_model2).launch(share=True)
+# Launch the interfaces simultaneously
+if __name__ == "__main__":
+    # demo_model1.launch(share=True)
+    demo_model1.launch(share=True)
+ 

gitattributes

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isatron_v3.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83fb9537440059cd0ffb1a34af740ac4874249b30d18c8d0a5b684cab433d740
+size 143125200

requirements.txt

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+gradio
+opencv-python
+numpy
+tensorflow
+keras==2.13.1
+keras_applications==1.0.8