app.py

import gradio as gr
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import requests

# Load the model and processor
model = CLIPModel.from_pretrained("geolocal/StreetCLIP")
processor = CLIPProcessor.from_pretrained("geolocal/StreetCLIP")

def classify_image(image):
    # Preprocess the image
    inputs = processor(images=image, return_tensors="pt")
    # Perform the inference
    outputs = model(**inputs)
    # Postprocess the outputs
    logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
    probs = logits_per_image.softmax(dim=1)  # we can use softmax to get probabilities
    return probs

# Define Gradio interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.inputs.Image(type="pil"),
    outputs="text",
    title="Geolocal StreetCLIP Classification",
    description="Upload an image to classify using Geolocal StreetCLIP"
)

# Launch the interface
iface.launch()