Update app.py

app.py

@@ -1,28 +1,20 @@
-from transformers import MobileNetV2FeatureExtractor, MobileNetV2ForImageClassification, MobileNetV2Config
-from PIL import Image
-
-# Replace with the path to the directory containing the model files
-model_folder = "mobilnetV2_ftprecmodelshuf.weights.best.hdf5"
-
-# Load the configuration
-config = MobileNetV2Config.from_pretrained(model_folder)
-
-# Create the feature extractor using the loaded configuration
-feature_extractor = MobileNetV2FeatureExtractor(config)
-
-# Load the model
-model = MobileNetV2ForImageClassification.from_pretrained(model_folder)
-
-# Load the image
-image = Image.open("test_image.png")
-
-# Preprocess the image
-inputs = feature_extractor(images=image, return_tensors="pt")
-
-# Make predictions
-outputs = model(**inputs)
-logits = outputs.logits
-
-# Model predicts one of the 1000 ImageNet classes
-predicted_class_idx = logits.argmax(-1).item()
-print("Predicted class:", model.config.id2label[predicted_class_idx])
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('export.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))}
+
+title = "Kapu"
+description = "An app for Chicken Disease Classisfication"
+article="<p style='text-align: center'>The app identifies and classifies three chicken diseases: Coccidiosis, Salmonella, and Newcastle, aiding in effective disease management for poultry farming.</p>"
+examples = ['test_image.jpg']
+interpretation='default'
+enable_queue=True
+
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()