Update app.py

app.py

@@ -1,41 +1,47 @@
 import gradio as gr
-import torch
 from transformers import pipeline
+import numpy as np
+import torch
 
-# Load the image captioning pipeline
+# Load the image-to-text model
 captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
 
-# Load Whisper model for speech synthesis
-pipe = pipeline("text-to-speech", model="openai/whisper-large-v3-turbo")
+# Load the Whisper model for speech synthesis
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "openai/whisper-large-v3-turbo"
+model = pipeline("text-to-speech", model=model_id, device=device)
 
-def launch(input_image):
+
+def process_image(input_image):
     try:
         # Step 1: Generate caption
-        out = captioner(input_image)
-        caption = out[0]['generated_text']
-        
+        caption = captioner(input_image)[0]['generated_text']
 
-        # Step 2: Synthesize speech from caption
-        speech = pipe(caption)
-        audio_data = speech['audio']
-        sample_rate = speech['sampling_rate']
+        # Step 2: Convert caption to speech
+        speech_output = model(caption)
+        audio_data = np.array(speech_output["audio"]).astype(np.float32)
+        sample_rate = speech_output["sampling_rate"]
 
         return (audio_data, sample_rate), caption
 
     except Exception as e:
-        return str(e), "Error in processing."
+        return str(e)
+
 
-# Gradio UI
+# Create Gradio Interface
 iface = gr.Interface(
-    fn=launch,
-    inputs=gr.Image(type='pil', label="Upload an Image"),
+    fn=process_image,
+    inputs=gr.Image(type='pil', label="Upload Image"),
     outputs=[
-        gr.Audio(type="numpy", label="Generated Audio"),
+        gr.Audio(label="Generated Speech"),
         gr.Textbox(label="Generated Caption")
     ],
-    title="Whisper Image to Audio",
+    title="Image to Audio with Caption",
     description="Upload an image to generate a caption and hear it described with speech."
 )
 
 iface.launch(share=True)
 
+