Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import json
+from tensorflow.keras.applications.efficientnet import preprocess_input
+from tensorflow.keras.preprocessing import image as keras_image
+
+# Load Model & Class Indices
+MODEL_PATH = "latest_model%2520%25281%2529.keras"
+CLASS_INDICES_PATH = "class_indices%2525252520%252525252811%2525252529 (1).json"
+FLOWER_INFO_PATH = "flower_info%2525252520%25252525281%2525252529[1].json"
+
+def load_model():
+    return tf.keras.models.load_model(MODEL_PATH)
+
+def load_class_indices():
+    with open(CLASS_INDICES_PATH, "r") as f:
+        return json.load(f)
+
+def load_flower_info():
+    with open(FLOWER_INFO_PATH, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+model = load_model()
+class_indices = load_class_indices()
+flower_info = load_flower_info()
+class_names = list(class_indices.keys())
+
+def preprocess_image(pil_image):
+    # Convert PIL image to numpy array and preprocess
+    img_array = keras_image.img_to_array(pil_image.resize((224, 224)))
+    img_array = np.expand_dims(img_array, axis=0)
+    return preprocess_input(img_array)
+
+def predict_image(pil_image):
+    img_array = preprocess_image(pil_image)
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions[0])]
+
+    info = flower_info.get(predicted_class, "No additional information available.")
+
+    return f"Identified as: {predicted_class}", info
+
+def predict(pil_image):
+    return predict_image(pil_image)
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),  # Receive image as a PIL object
+    outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Flower Information")],
+    title="Flower Identification App",
+    description="Upload an image of a flower to identify it and get care information."
+)
+
+if __name__ == "__main__":
+    interface.launch()