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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}**")
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