Update app.py

app.py

@@ -6,9 +6,7 @@ from tensorflow.keras.preprocessing.image import img_to_array
 import numpy as np
 from PIL import Image
 import os
-import requests
 
-# Load your model and tokenizer
 labels = {
     'class': ['amphibia', 'aves', 'invertebrates', 'lacertilia', 'mammalia', 'serpentes', 'testudines'],
     'serpentes': ["Butler's Gartersnake", "Dekay's Brownsnake", 'Eastern Gartersnake', 'Eastern Hog-nosed snake', 'Eastern Massasauga', 'Eastern Milksnake', 'Eastern Racer Snake', 'Eastern Ribbonsnake', 'Gray Ratsnake', "Kirtland's Snake", 'Northern Watersnake', 'Plains Gartersnake', 'Red-bellied Snake', 'Smooth Greensnake'],
@@ -17,44 +15,26 @@ labels = {
     'amphibia': ['American Bullfrog', 'American Toad', 'Green Frog', 'Northern Leopard Frog']
 }
 
-model_url = "https://github.com/sowbaranika1302/ohio_classifier/raw/main/Models/inceptionv3_best_withoutblanks.h5"
-model_path = "inceptionv3_best_withoutblanks.h5"
 
-# Download the model if it doesn't exist locally
-if not os.path.exists(model_path):
-    response = requests.get(model_url)
-    with open(model_path, 'wb') as f:
-        f.write(response.content)
-
-# Load the model
-model = load_model(model_path)
-
-# Preprocess the image
 def preprocess_image(image):
-    img = image.resize((224, 224))  # MobileNet requires 224x224 input size
+    img = image.resize((224, 224))
     img_array = img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-    img_array = preprocess_input(img_array)  # Preprocess the image
-    return img_array, img  # Return both the preprocessed array and resized image for display
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+    return img_array, img
 
-# Function to perform inference
 def predict(img):
     img_array, resized_img = preprocess_image(img)
-    #model = load_model(r"inceptionv3_classs.h5")
-    preds = model.predict(img_array)  # Get the model predictions
+    model = load_model("inceptionv3_class.h5")
+    preds = model.predict(img_array)
     decoded_preds = np.argmax(preds)
-    
-    # Return the prediction and the resized image for display
     return resized_img, labels['class'][decoded_preds]
 
-# Create the Gradio interface
 iface = gr.Interface(
-    fn=predict,  # The prediction function
-    
-    inputs=gr.Image(type="pil"),  # Input as a PIL image
-    outputs=[gr.Image(type="pil"), gr.Label(label="species_label")],  # Output the resized image and prediction
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Image(type="pil"), gr.Label(label="species_label")],
     title="Animal Classifier"
 )
 
-# Launch the Gradio interface
 iface.launch()