import os
import torch
import streamlit as st
from PIL import Image
from torchvision import transforms

torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")

model = torch.hub.load('pytorch/vision:v0.9.0', 'densenet121', pretrained=True)
model.eval()

# Download ImageNet labels
os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")

def inference(input_image):
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    input_tensor = preprocess(input_image)
    input_batch = input_tensor.unsqueeze(0)  # create a mini-batch as expected by the model

    # move the input and model to GPU for speed if available
    if torch.cuda.is_available():
        input_batch = input_batch.to('cuda')
        model.to('cuda')

    with torch.no_grad():
        output = model(input_batch)
    
    # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
    probabilities = torch.nn.functional.softmax(output[0], dim=0)

    # Read the categories
    with open("imagenet_classes.txt", "r") as f:
        categories = [s.strip() for s in f.readlines()]

    # Show top categories per image
    top5_prob, top5_catid = torch.topk(probabilities, 5)
    result = {}
    for i in range(top5_prob.size(0)):
        result[categories[top5_catid[i]]] = top5_prob[i].item()

    return result

# Streamlit app
st.title("DenseNet Image Classification Demo")
st.sidebar.title("Upload Image")

# File upload
uploaded_file = st.sidebar.file_uploader("Choose an image...", type="jpg")

if uploaded_file is not None:
    # Display the uploaded image
    st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)

    # Inference
    result = inference(Image.open(uploaded_file))
    
    # Display results
    st.subheader("Top Predictions:")
    for category, confidence in result.items():
        st.write(f"{category}: {confidence:.2%}")