app.py

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

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

def classify_image(image):
    # Example labels for classification
    labels = ["a photo of a cat", "a photo of a dog", "a photo of a car", "a photo of a tree"]
    
    # Preprocess the image and text
    inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
    
    # 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
    
    # Convert the probabilities to a list
    probs_list = probs.tolist()[0]
    
    # Create a dictionary of labels and probabilities
    result = {label: prob for label, prob in zip(labels, probs_list)}
    
    return result

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

# Launch the interface
iface.launch()