Create app.py

app.py

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+import torch
+import gradio as gr
+from PIL import Image
+import scipy.io.wavfile as wavfile
+from transformers import pipeline
+
+# Set device for processing
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load models
+caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
+narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
+
+def generate_audio(text):
+    """Generate audio narration from text."""
+    narrated_text = narrator(text)
+    wavfile.write("output.wav", rate=narrated_text["sampling_rate"], data=narrated_text["audio"][0])
+    return "output.wav"
+
+def caption_my_image(pil_image):
+    """Generate caption for the image and convert it to audio."""
+    semantics = caption_image(images=pil_image)[0]['generated_text']
+    return generate_audio(semantics)
+
+# Define the Gradio interface
+demo = gr.Interface(
+    fn=caption_my_image,
+    inputs=[gr.Image(label="Upload Your Image", type="pil")],
+    outputs=[gr.Audio(label="Generated Audio Caption")],
+    title="Image Captioning and Narration",
+    description=(
+        "Upload an image to generate a descriptive caption and listen to its narration. "
+        "This application is powered by AI tools and brought to you by **Taizun**."
+    ),
+    theme="compact"  # Use a minimalistic theme
+)
+
+# Launch the application
+demo.launch()