Create app.py

app.py

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+import streamlit as st
+from transformers import ViTForImageClassification, ViTFeatureExtractor
+from PIL import Image
+import torch
+import matplotlib.pyplot as plt
+
+# Define the repository ID
+repo_id = "Hammad712/5-Flower-Types-Classification-VIT-Model"
+
+# Load the model and feature extractor
+model = ViTForImageClassification.from_pretrained(repo_id)
+feature_extractor = ViTFeatureExtractor.from_pretrained(repo_id)
+
+# Define the class names dictionary
+class_names = {0: 'Lilly', 1: 'Lotus', 2: 'Orchid', 3: 'Sunflower', 4: 'Tulip'}
+
+# Define the inference function
+def predict(image):
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.nn.functional.softmax(logits, dim=-1).squeeze().tolist()
+    predicted_class_idx = logits.argmax(-1).item()
+    predicted_class_name = class_names[predicted_class_idx]
+    return probabilities, predicted_class_name
+
+# Streamlit app
+st.title("Flower Type Classification")
+st.write("Upload an image of a flower to classify its type.")
+
+# Upload image
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    
+    # Predict the class of the image
+    probabilities, predicted_class = predict(image)
+    
+    # Display the probabilities in a bar chart
+    fig, ax = plt.subplots()
+    ax.bar(class_names.values(), probabilities)
+    ax.set_ylabel('Probability')
+    ax.set_xlabel('Class')
+    ax.set_title('Class Probabilities')
+    st.pyplot(fig)
+    
+    # Display the predicted class
+    st.write(f"Predicted class: **{predicted_class}**")