Create app.py

app.py

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+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import gradio as gr
+import pickle
+import os
+
+# Define image dimensions (must match training dimensions)
+img_height = 224
+img_width = 224
+
+# Load the trained Keras model from export.pkl
+# Ensure 'export.pkl' is in the same directory as app.py
+try:
+    with open('export.pkl', 'rb') as file:
+        model = pickle.load(file)
+    print("Model loaded from 'export.pkl' successfully.")
+except FileNotFoundError:
+    print("Error: 'export.pkl' not found. Make sure the model file exists.")
+    exit()
+except Exception as e:
+    print(f"Error loading model: {e}")
+    exit()
+
+# Define the class indices mapping explicitly
+# This needs to match the order used by ImageDataGenerator during training
+class_indices_map = {'Non Venomous': 0, 'Venomous': 1}
+# Reverse the map to get class names from indices for prediction output
+label_map = {v: k for k, v in class_indices_map.items()}
+
+def predict_snake_class(img):
+    # 1. Convert to PIL Image (if not already) and resize
+    if not isinstance(img, Image.Image):
+        img = Image.fromarray(img)
+    img = img.resize((img_width, img_height))
+
+    # 2. Convert to NumPy array
+    img_array = np.array(img)
+
+    # 3. Normalize the image
+    img_array = img_array / 255.0
+
+    # 4. Expand dimensions to create a batch
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # 5. Make a prediction
+    predictions = model.predict(img_array)
+
+    # 6. Determine the predicted class and confidence
+    predicted_class_index = np.argmax(predictions[0])
+    confidence_score = predictions[0][predicted_class_index]
+
+    predicted_class_name = label_map[predicted_class_index]
+
+    # Format output for Gradio Label component
+    return {label_map[i]: float(predictions[0][i]) for i in range(len(label_map))}
+
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=predict_snake_class,  # Our prediction function
+    inputs=gr.Image(type="pil", label="Upload Snake Image"),  # Input is an image
+    outputs=gr.Label(num_top_classes=2),  # Output is a label with top 2 classes
+    title="Snake Classification Model",
+    description="Upload an image of a snake to predict if it's Non Venomous or Venomous.",
+    live=True
+)
+
+# Launch the interface
+iface.launch(share=True)
+