### TODO:
# 1. gradio interface to upload image
# 2. use image ai model to determine if feather is from protected-birds.csv file
# 3. prompt user to verify results if bird is protected for themselves before making a report
# 4. list possible protected birds from list of protected-birds.csv file along with confidence score
# 5. output the result in a table format along with search queries for each bird for user to verify
# 6. prompt user to make a report they are confident the feather in the image is from a protected bird along with the link to https://www.fws.gov/contact-us

import gradio as gr
import pandas as pd
import numpy as np
import os
from PIL import Image
import requests
from io import BytesIO
from imageai.Prediction import ImagePrediction
import csv

# Load the protected-birds.csv file
protected_birds = pd.read_csv('protected-birds.csv')
protected_birds = protected_birds.dropna()
protected_birds = protected_birds['Common Name'].str.lower().values

# Load the image ai model
model = ImagePrediction()
model.setModelTypeAsResNet()
model.setModelPath("resnet50_weights_tf_dim_ordering_tf_kernels.h5")
model.loadModel()

# Function to check if the feather is from a protected bird
def check_feather(image):
    # Load the image
    response = requests.get(image)
    img = Image.open(BytesIO(response.content))
    img.save('image.jpg')
    
    # Make a prediction
    model_predictions, probabilities = model.predictImage('image.jpg', result_count=5)
    
    # Check if the feather is from a protected bird
    protected = False
    protected_birds_list = []
    for bird in protected_birds:
        for prediction in model_predictions:
            if bird in prediction.lower():
                protected = True
                protected_birds_list.append([bird, prediction, probabilities[model_predictions.index(prediction)]])
    
    return protected, protected_birds_list

# Gradio interface
image = gr.inputs.Image()
gr.Interface(fn=check_feather, inputs=image, outputs="text").launch()