initial space commit

app.py

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+ ### 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()
+

requirements.txt

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+gradio
+pandas
+numpy
+Pillow
+requests
+imageai
+tensorflow>=2.4.0
+pytest
+mock
+coverage