Create app.py

app.py

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+# to create nueral network
+import torch
+
+# for interface
+import gradio as gr
+
+# to open images
+from PIL  import Image
+
+# used for audio
+import scipy.io.wavfile as wavfile
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
+
+# Load the pretrained weights
+caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
+
+
+# Define the function to generate audio from text 
+def generate_audio(text):
+    # Generate the narrated text
+    narrated_text = narrator(text)
+
+    # Save the audio to WAV file
+    wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
+                  data=narrated_text["audio"][0])
+
+    # Return the path to the saved output WAV file
+    return "output.wav" # return audio
+
+def caption_my_image(pil_image):
+
+    semantics = caption_image(images=pil_image)[0]['generated_text']
+    audio = generate_audio(semantics)
+    return semantics,audio  # returns both text and audio output
+
+
+demo = gr.Interface(fn=caption_my_image,
+                    inputs=[gr.Image(label="Select Image",type="pil")],
+                    outputs=[ 
+                        gr.Textbox(label="Image Caption"),
+                        gr.Audio(label="Image Caption Audio")],
+                    title="IMAGE CAPTIONING WITH AUDIO OUTPUT",
+                    description="THIS APPLICATION WILL BE USED TO CAPTION IMAGES WITH THE HELP OF AI")
+demo.launch()