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| import gradio as gr | |
| import requests | |
| from PIL import Image | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| import requests | |
| import base64 | |
| import tempfile | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cpu") | |
| class Aspecto(): | |
| pass | |
| screen = Aspecto() | |
| with gr.Blocks(theme=gr.themes.Ocean(primary_hue="pink", neutral_hue="indigo", font=[gr.themes.GoogleFont("Montserrat"), "Playwrite England SemiJoine", "Quicksand"])) as demo: | |
| textbox = gr.Textbox(label="Url") | |
| with gr.Row(): | |
| button = gr.Button("Describir", variant="primary") | |
| clear = gr.Button("Borrar") | |
| output = gr.Textbox(label="Resumen") | |
| with gr.Row(): | |
| button2 = gr.Button("Leer", variant="primary") | |
| clear = gr.Button("Borrar") | |
| output2 = gr.Audio(label="Audio") | |
| def describir(url): | |
| raw_image = Image.open(requests.get(url, stream=True).raw).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt").to("cpu") | |
| out = model.generate(**inputs) | |
| return processor.decode(out[0], skip_special_tokens=True) | |
| def leer(texto): | |
| response = requests.post("https://charly-text-to-speech.hf.space/run/predict", json={ | |
| "data": [ | |
| texto, | |
| ]}).json() | |
| data = response['data'][0] | |
| # Extraer la parte de base64 del string (eliminar el prefijo 'data:audio/flac;base64,') | |
| audio_base64 = data.split(',')[1] | |
| # Decodificar el string base64 | |
| audio_data = base64.b64decode(audio_base64) | |
| # Crear un archivo temporal | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.flac') as temp_audio_file: | |
| temp_audio_file.write(audio_data) | |
| temp_audio_path = temp_audio_file.name | |
| return temp_audio_path | |
| button.click(describir, [textbox], output) | |
| button2.click(leer, [output], output2) | |
| demo.launch(debug=True) |