app.py

import gradio as gr
from transformers import CLIPProcessor, CLIPModel, pipeline
import torch
from PIL import Image
import scipy.io.wavfile

# Load the MusicGen model
musicgen = pipeline("text-to-audio", model="facebook/musicgen-small")

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

labels = ['Albania', 'Andorra', 'Argentina', 'Australia', 'Austria', 'Bangladesh', 'Belgium', 'Bermuda', 'Bhutan', 'Bolivia', 'Botswana', 'Brazil', 'Bulgaria', 'Cambodia', 'Canada', 'Chile', 'China', 'Colombia', 'Croatia', 'Czech Republic', 'Denmark', 'Dominican Republic', 'Egypt', 'Ecuador', 'Estonia', 'Finland', 'France', 'Germany', 'Ghana', 'Greece', 'Greenland', 'Guam', 'Guatemala', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Ireland', 'Israel', 'Italy', 'Japan', 'Jordan', 'Kenya', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lesotho', 'Lithuania', 'Luxembourg', 'Macedonia', 'Madagascar', 'Malaysia', 'Malta', 'Mexico', 'Monaco', 'Mongolia', 'Montenegro', 'Netherlands', 'New Zealand', 'Nigeria', 'Norway', 'Pakistan', 'Palestine', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Romania', 'Russia', 'Rwanda','Saudi Arabia', 'Senegal', 'Serbia', 'Singapore', 'Slovakia', 'Slovenia', 'South Africa', 'South Korea', 'Spain', 'Sri Lanka', 'Swaziland', 'Sweden', 'Switzerland', 'Syria','Taiwan', 'Thailand', 'Tunisia', 'Turkey', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uruguay']

def process_image(image, audio_path="musicgen_out.wav"):
    # Ensure the image is in the correct format
    if isinstance(image, str):
        image = Image.open(image)

    # Process the image and text inputs
    inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)

    # Get the model outputs
    with torch.no_grad():
        outputs = model(**inputs)
    logits_per_image = outputs.logits_per_image
    probs = logits_per_image.softmax(dim=1)

    # Get the country with the highest probability
    country_index = probs.argmax(dim=1).item()
    country = labels[country_index]

    # Generate music based on the country
    music_description = f"Traditional music from {country}"
    music = musicgen(music_description, forward_params={"do_sample": True})

    # Save the generated music to the specified path
    scipy.io.wavfile.write(audio_path, rate=music["sampling_rate"], data=music["audio"])

    # Return the country and the path to the generated music
    return country, audio_path

# Define the Gradio interface
inputs = gr.Image(type="pil", label="Upload a photo (تحميل صورة)")
outputs = [gr.Textbox(label="Country (البلد)"), gr.Audio(label="Generated Music (الموسيقى المولدة)")]

iface = gr.Interface(
    fn=process_image,
    inputs=inputs,
    outputs=outputs,
    title="Photo to Country and Music Generator محدد الموقع من الصور بالاضافة الى انشاء م",
    description="Upload a photo to identify the country and generate traditional music from that country. (قم بتحميل صورة لتحديد البلد وإنشاء موسيقى تقليدية من هذا البلد.)",
    examples=["/content/Egypt.jfif", "/content/Riyadh.jpeg", "/content/Syria.jfif", "/content/Turkey.jfif"]
)

# Launch the interface
iface.launch(debug=True)