Create app.py

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+import torch
+import soundfile as sf
+from diffusers import StableAudioPipeline
+import torchsde
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda")
+pipe = StableAudioPipeline.from_pretrained("stabilityai/stable-audio-open-1.0", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+
+#img_url = 'https://www.caracteristicass.de/wp-content/uploads/2023/02/imagenes-artisticas.jpg'
+
+
+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("Intro", variant="primary")
+        button2 = gr.Button("Leer", variant="primary")
+        clear = gr.Button("Borrar")
+    output = gr.Textbox(label="Resumen")
+    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("cuda")
+      out = model.generate(**inputs)
+      return processor.decode(out[0], skip_special_tokens=True)
+
+    def leer(texto):
+        prompt = texto
+        negative_prompt = "Low quality."
+
+        # set the seed for generator
+        generator = torch.Generator("cuda").manual_seed(0)
+
+        # run the generation
+        audio = pipe(
+            prompt,
+            negative_prompt=negative_prompt,
+            num_inference_steps=200,
+            audio_end_in_s=10.0,
+            num_waveforms_per_prompt=3,
+            generator=generator,
+        ).audios
+
+        salida = audio[0].T.float().cpu().numpy()
+        #sf.write("demo.wav", salida, pipe.vae.sampling_rate)
+        return (salida,pipe.vae.sampling_rate)
+
+
+    button.click(describir, [textbox], output)
+    button.click(leer, [output], output2)
+
+demo.launch(debug=True)