Rename app.py to app

app

@@ -0,0 +1,21 @@
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+learn = load_learner('model.pkl')
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Label(num_top_classes=3)
+)
+
+# Enable the queue to handle POST requests
+interface.queue(api_open=True)
+
+# Launch the interface
+interface.launch()

app.py

@@ -1,46 +0,0 @@
-import gradio as gr
-import pickle
-import numpy as np
-from PIL import Image
-
-# Load the trained model
-with open("bird_classifier.pkl", "rb") as f:
-    model = pickle.load(f)
-
-# Get class names automatically from the model
-try:
-    class_names = model.classes_  # Works for scikit-learn models
-except AttributeError:
-    # If the model doesn't have classes_, you'd need a fallback or custom logic
-    raise ValueError("Model does not have 'classes_' attribute. Please provide class names manually or adjust the code.")
-
-# Define the prediction function
-def classify_bird(image):
-    # Preprocess the image (adjust this based on how your model was trained)
-    img = Image.fromarray(image.astype("uint8"), "RGB")  # Convert to PIL Image
-    img = img.resize((224, 224))  # Example resize, adjust to your model's input size
-    img_array = np.array(img) / 255.0  # Normalize if your model expects this
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-
-    # Make prediction
-    prediction = model.predict(img_array)  # Adjust based on your model's method
-    
-    # Handle prediction output
-    if len(prediction.shape) > 1:  # If prediction is a probability array (e.g., softmax output)
-        predicted_class = class_names[np.argmax(prediction)]
-    else:  # If prediction is a single class index (e.g., scikit-learn's default)
-        predicted_class = class_names[prediction[0]]
-    
-    return predicted_class
-
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=classify_bird,                # Prediction function
-    inputs=gr.Image(type="numpy"),   # Input is an image, returned as NumPy array
-    outputs=gr.Textbox(),            # Output is text (bird species)
-    title="Bird Classifier",
-    description="Upload an image of a bird and get its species predicted!"
-)
-
-# Launch the app
-interface.launch()