Create app.py

app.py

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+import streamlit as st
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import torch
+
+# Load model and feature extractor
+MODEL_NAME = "google/vit-base-patch16-224"
+feature_extractor = ViTFeatureExtractor.from_pretrained(MODEL_NAME)
+model = ViTForImageClassification.from_pretrained(MODEL_NAME)
+
+# Streamlit UI
+st.title("Animal Recognition App")
+st.write("Upload an image, and the model will identify the animal.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Preprocess image
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    
+    # Predict
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+    
+    # Get label
+    label = model.config.id2label[predicted_class_idx]
+    st.success(f"Predicted Animal: **{label}**")