Create app.py

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+from fastai.vision.all import *
+from fastcore.all import *
+
+learn = load_learner("animal_species_detection.pkl")
+
+categories = ("cat","horse","rabbit")
+
+def classify_img(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+with gr.Blocks(title ="which animal is this ?") as demo:
+    with gr.Row():
+        gr.Markdown("""
+                     ## Animal species detection
+                     #### upload photo!
+                     #### click on the photos to try
+                     """)
+    with gr.Row():
+        image = gr.inputs.Image(shape=(192,192))   
+    with gr.Row():
+        output = gr.outputs.Label()      
+    with gr.Row():
+        image_button = gr.Button("PREDİCT")
+        image_button.click(classify_img, inputs=image, outputs=output)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Examples(inputs=image,examples=["r1.jpg"],label="rabbit")
+        with gr.Column():
+            gr.Examples(inputs=image,examples=["r2.jpg"],label="rabbit")
+        with gr.Column():
+            gr.Examples(inputs=image,examples=["r2.jpg"],label="rabbit")
+        with gr.Column():
+                gr.Examples(inputs=image,examples=["h1.jpg"],label="horse")  
+        with gr.Column():
+                gr.Examples(inputs=image,examples=["h2.jpg"],label="horse") 
+        with gr.Column():
+                gr.Examples(inputs=image,examples=["h3.jpg"],label="horse") 
+        with gr.Column():
+                gr.Examples(inputs=image,examples=["c1.jpg"],label="cat") 
+        with gr.Column():
+                gr.Examples(inputs=image,examples=["c2.jpg"],label="cat")
+        with gr.Column():
+                gr.Examples(inputs=image,examples=["c3.jpg"],label="cat")
+    
+demo.launch()   
+