Update app.py

app.py

@@ -1,20 +1,20 @@
-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
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-caption_image = pipeline("image-to-text",
-                model="Salesforce/blip-image-captioning-large", device=device)
-def caption_my_image(pil_image):
-    semantics = caption_image(images=pil_image)[0]['generated_text']
-    return semantics
-demo = gr.Interface(fn=caption_my_image,
-                    inputs=[gr.Image(label="Select Image",type="pil")],
-                    outputs=[gr.Textbox(label="Image Caption")],
-                    title="@GenAILearniverse Project 8: Image Captioning",
-                    description="THIS APPLICATION WILL BE USED TO CAPTION THE IMAGE.")
+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
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+caption_image = pipeline("image-to-text",
+                model="Salesforce/blip-image-captioning-large", device=device)
+def caption_my_image(pil_image):
+    semantics = caption_image(images=pil_image)[0]['generated_text']
+    return semantics
+demo = gr.Interface(fn=caption_my_image,
+                    inputs=[gr.Image(label="Select Image",type="pil")],
+                    outputs=[gr.Textbox(label="Image Caption")],
+                    title="Image Captioning",
+                    description="THIS APPLICATION WILL BE USED TO CAPTION THE IMAGE.")
 demo.launch()