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
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+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 the narrated text
+    narrated_text = narrator(text)
+
+    # Save the audio to a WAV file
+    wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
+                  data=narrated_text["audio"][0])
+    # Return the path to the saved audio file
+    return "output.wav"
+
+
+def caption_my_image(pil_image):
+    semantics = caption_image(images=pil_image)[0]['generated_text']
+    return generate_audio(semantics)
+
+
+demo = gr.Interface(
+    fn=caption_my_image,
+    inputs=[gr.Image(label="Select Image", type="pil")],
+    outputs=[gr.Audio(label="Image Caption")],
+    title="Project 07: Image Captioning",
+    description="As understood from the title, if not already, this application will caption your image"
+)
+
+demo.launch()