app.py

# 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 = "model.keras"
# CLASS_INDICES_PATH = "class_indices.json"
# FLOWER_INFO_PATH = "flower_info.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()
# 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 = "model.keras"
# CLASS_INDICES_PATH = "class_indices.json"
# FLOWER_INFO_PATH = "flower_info.json"

# def load_model():
#     try:
#         return tf.keras.models.load_model(MODEL_PATH)
#     except Exception as e:
#         print(f"Error loading model: {e}")
#         return None

# 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):
#     try:
#         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
#     except Exception as e:
#         return "Error in prediction", str(e)

# 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.",
#     theme="compact"  # Optional: Use a compact theme to reduce UI clutter
# )

# if __name__ == "__main__":
#     interface.launch(share=True, api=True)  # Add api=True if you need API access

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 = "model.keras"
CLASS_INDICES_PATH = "class_indices.json"
FLOWER_INFO_PATH = "flower_info.json"

def load_model():
    try:
        return tf.keras.models.load_model(MODEL_PATH)
    except Exception as e:
        print(f"Error loading model: {e}")
        return None

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):
    try:
        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
    except Exception as e:
        return "Error in prediction", str(e)

def predict(pil_image):
    return predict_image(pil_image)

# Launch the Gradio interface
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(share=True)  # No api=True here