Upload 5 models

Dockerfile

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+
+FROM python:3.8-slim
+
+
+WORKDIR /app
+
+COPY requirements.txt requirements.txt
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+
+# Load the model
+try:
+    model = load_model('wound_classifier_model_googlenet.h5')
+except Exception as e:
+    raise RuntimeError(f"Error loading model: {e}")
+
+# Define the input shape
+input_shape = (224, 224, 3)
+
+def preprocess_image(image, target_size):
+    """
+    Preprocess the input image for the model.
+    - Resize the image to the target size.
+    - Normalize pixel values to the range [0, 1].
+    """
+    if image is None:
+        raise ValueError("No image provided")
+    image = image.convert("RGB")  # Ensure the image is in RGB mode
+    image = image.resize(target_size)
+    image_array = np.array(image)
+    image_array = image_array / 255.0  # Normalize the image
+    return image_array
+
+def predict(image):
+    """
+    Predict the class probabilities for the input image.
+    - Preprocess the image.
+    - Predict using the loaded model.
+    - Return results as a dictionary with class labels and probabilities.
+    """
+    try:
+        # Preprocess the image
+        input_data = preprocess_image(image, (input_shape[0], input_shape[1]))
+        input_data = np.expand_dims(input_data, axis=0)  # Add batch dimension
+
+        # Load class labels
+        try:
+            with open('./classes.txt', 'r') as file:
+                class_labels = file.read().splitlines()
+        except FileNotFoundError:
+            raise RuntimeError("Class labels file 'classes.txt' not found.")
+        
+        if len(class_labels) != model.output_shape[-1]:
+            raise ValueError("Mismatch between model output and class labels.")
+
+        # Predict probabilities
+        predictions = model.predict(input_data)
+        results = {class_labels[i]: float(predictions[0][i]) for i in range(len(class_labels))}
+        return results
+    except Exception as e:
+        return {"error": str(e)}
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"), 
+    outputs=gr.Label(num_top_classes=18),  # Adjust num_top_classes as needed
+    live=True
+)
+
+# Launch the Gradio interface
+iface.launch(server_name="0.0.0.0", server_port=7860)

classes.txt

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+Abrasions: 0
+Bruises: 1
+Burns: 2
+Cut: 3
+Diabetic Wounds: 4
+Gingivitis: 5
+Surgical Wounds: 6
+Venous Wounds: 7
+athlete foot: 8
+cellulitis: 9
+chickenpox: 10
+cutaneous larva migrans: 11
+impetigo: 12
+nail fungus: 13
+ringworm: 14
+shingles: 15
+tooth discoloration: 16
+ulcer: 17

wound_classifier_model_googlenet.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9f89fe0041825789e66caf712515f1265f43b05b5b843db2da5840f30a7abcbe
+size 113524192