Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import torch
+from transformers import pipeline
+import time
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+    .title {
+        font-size: 40px;
+        color: #4CAF50;
+        font-weight: bold;
+        text-align: center;
+    }
+    .sub-title {
+        font-size: 20px;
+        color: #333;
+        text-align: center;
+    }
+    .upload-button {
+        background-color: #4CAF50;
+        color: white;
+        border-radius: 5px;
+        padding: 10px 20px;
+        font-size: 18px;
+    }
+    .upload-button:hover {
+        background-color: #45a049;
+    }
+    .prediction {
+        font-size: 22px;
+        font-weight: bold;
+        color: #388E3C;
+        text-align: center;
+    }
+    .confidence {
+        font-size: 18px;
+        color: #555;
+        text-align: center;
+    }
+    .image-container {
+        display: flex;
+        justify-content: center;
+        margin-top: 30px;
+    }
+    .footer {
+        text-align: center;
+        margin-top: 50px;
+        font-size: 14px;
+        color: #777;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Load model from Hugging Face
+@st.cache_resource
+def load_model():
+    model = pipeline('image-classification', model='google/vit-base-patch16-224-in21k')
+    return model
+
+model = load_model()
+
+# Streamlit app UI
+st.markdown('<p class="title">🌱 Plant Identification App 🌱</p>', unsafe_allow_html=True)
+st.markdown('<p class="sub-title">Upload a plant image and let the app identify its species!</p>', unsafe_allow_html=True)
+
+# File uploader for plant image
+uploaded_file = st.file_uploader("Choose a plant image...", type=["jpg", "jpeg", "png"], label_visibility="collapsed")
+
+if uploaded_file is not None:
+    # Show the uploaded image with a loading effect
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Plant Image.", use_column_width=True)
+
+    # Display loading indicator and classify
+    with st.spinner('Classifying plant species...'):
+        time.sleep(2)  # Simulate loading time
+        predictions = model(image)
+
+    # Display prediction result
+    st.markdown(f'<p class="prediction">Predicted Species: {predictions[0]["label"]}</p>', unsafe_allow_html=True)
+    st.markdown(f'<p class="confidence">Confidence: {predictions[0]["score"]*100:.2f}%</p>', unsafe_allow_html=True)
+
+# Footer for extra info or tips
+st.markdown('<div class="footer">Powered by Streamlit 🌸</div>', unsafe_allow_html=True)
+