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.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 fungi-model_transferlearning_xception.keras filter=lfs diff=lfs merge=lfs -text
+images/cv[[:space:]](2).png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+ 
+model_path = "fungi-model_transferlearning_xception.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+
+def predict_fungi(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
+
+    prediction = model.predict(image)
+ 
+
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 2)
+ 
+    # Separate the probabilities for each class
+    p_ediblemushroomsporocarp = prediction[0][0]  
+    p_ediblesporocarp = prediction[0][1]   
+    p_poisonousmushroomsporocarp = prediction[0][2]    
+    p_poisonoussporocarp = prediction[0][2]  
+ 
+    return {'Edible Mushroom Sporocarp':  p_ediblemushroomsporocarp, 'Edible Sporocarp': p_ediblesporocarp, 'Poisonous Mushroom Sporocarp': p_poisonousmushroomsporocarp, 'Poisonous Sporocarp': p_poisonoussporocarp}
+ 
+ 
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_fungi,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/ce (1).jpg", "images/ce (2).jpg", "images/ncvc (1).png", "images/ncvc (5).png", "images/nncv (2).png", "images/nncv (4).png", "images/cv (2).png", "images/cv (4).png"],
+    description="TEST.")
+ 
+iface.launch()