Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# Load an animal classification model
+animal_pipeline = pipeline(task="image-classification", model="microsoft/resnet-50")
+
+st.title("Animal Species Classifier 🐾")
+
+# Upload the animal image
+file_name = st.file_uploader("Upload an animal image")
+
+if file_name is not None:
+    col1, col2 = st.columns(2)
+
+    # Display the uploaded image
+    image = Image.open(file_name)
+    col1.image(image, use_container_width=True, caption="Uploaded Image")
+
+    # Make predictions
+    predictions = animal_pipeline(image)
+
+    # Display predictions and habitat information
+    col2.header("Animal Predictions 🐾")
+    habitat_info = {
+        "tiger": "Forests, grasslands, and mangroves in Asia.",
+        "elephant": "Grasslands and forests in Africa and Asia.",
+        "penguin": "Antarctic and coastal areas in the Southern Hemisphere.",
+        "kangaroo": "Open plains, woodlands, and savannas in Australia.",
+        "panda": "Bamboo forests in China.",
+    }
+
+    for p in predictions[:3]:
+        species = p['label'].lower()
+        confidence = round(p['score'] * 100, 1)
+        col2.subheader(f"**{species.capitalize()}**: {confidence}%")
+        
+        # Show habitat info if available
+        habitat = habitat_info.get(species, "Habitat information not available.")
+        col2.write(f"**Habitat:** {habitat}")