Update app

app

@@ -1,32 +1,40 @@
 import gradio as gr
-from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 import torch
+import pickle
+import torchvision.transforms as transforms
 
-# Load your model and processor (replace with your model’s Hugging Face ID or local path)
-model_name = "your-username/your-bird-classifier"  # e.g., "dennisjooo/Birds-Classifier-EfficientNetB2"
-processor = AutoImageProcessor.from_pretrained(model_name)
-model = AutoModelForImageClassification.from_pretrained(model_name)
+# Load the pickled model
+model_path = "birds_classifier.pkl"
+try:
+    with open(model_path, "rb") as f:
+        model = pickle.load(f)
+    model.eval()  # Set model to evaluation mode
+except Exception as e:
+    raise Exception(f"Failed to load model: {str(e)}")
 
-# Prediction function
-def classify_bird(image):
-    # Process the image
-    inputs = processor(image, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs).logits
-    # Get the predicted label
-    predicted_idx = outputs.argmax(-1).item()
-    label = model.config.id2label[predicted_idx]
-    return f"Predicted bird species: {label}"
+# Define image preprocessing (adjust these transforms based on your model's training)
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),  # Match your model's expected input size
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # ImageNet defaults
+])
 
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=classify_bird,
-    inputs=gr.Image(type="pil", label="Upload a bird image"),
-    outputs=gr.Textbox(label="Prediction"),
-    title="Bird Species Classifier",
-    description="Upload an image of a bird to identify its species!"
-)
+# Replace with your actual list of bird species (in the order the model was trained)
+class_labels = ["Sparrow", "Eagle", "Blue Jay", "Cardinal"]  # Update this!
 
-# Launch the app
-interface.launch()
+# Prediction function
+def classify_bird(image):
+    try:
+        if image is None:
+            return "Please upload an image of a bird."
+        
+        # Preprocess the uploaded image
+        img = preprocess(image).unsqueeze(0)  # Add batch dimension
+        
+        # Make prediction automatically
+        with torch.no_grad():
+            outputs = model(img)  # Model outputs logits or probabilities
+        
+        # Get the predicted species
+        predicted_idx = outputs.argmax(-1).item()