Update app.py

app.py

@@ -1,32 +1,42 @@
-import gradio as gr
-import requests
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-import time
-
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
-
-def caption(img, min_len, max_len):
-    raw_image = Image.open(img).convert('RGB')
-  
-    inputs = processor(raw_image, return_tensors="pt")
-    
-    out = model.generate(**inputs, min_length=min_len, max_length=max_len)
-    return processor.decode(out[0], skip_special_tokens=True)
-
-def greet(img, min_len, max_len):
-    start = time.time()
-    result = caption(img, min_len, max_len)
-    end = time.time()
-    total_time = str(end - start)
-    result = result + '\n' + total_time + ' seconds'
-    return result
-
-iface = gr.Interface(fn=greet, 
-                     title='', 
-                     description=" ",
-                     inputs=[gr.Image(type='filepath', label='Image'), gr.Slider(label='Minimum Length', minimum=1, maximum=1000, value=30), gr.Slider(label='Maximum Length', minimum=1, maximum=1000, value=100)], 
-                     outputs=gr.Textbox(label='Caption'),
-                     theme = gr.themes.Base(primary_hue="teal",secondary_hue="teal",neutral_hue="slate"),)
+import gradio as gr
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+import time
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+def caption(img, min_len, max_len):
+    raw_image = Image.open(img).convert('RGB')
+  
+    inputs = processor(raw_image, return_tensors="pt")
+    
+    out = model.generate(**inputs, min_length=min_len, max_length=max_len)
+    return processor.decode(out[0], skip_special_tokens=True)
+
+def greet(img, min_len, max_len):
+    start = time.time()
+    result = caption(img, min_len, max_len)
+    end = time.time()
+    total_time = str(end - start)
+    result = result + '\n' + total_time + ' seconds'
+    return result
+
+iface = gr.Interface(fn=greet, 
+                     title='Image Captioning with BLIP', 
+                     description="Generate captions for images using the BLIP model.",
+                     inputs=[gr.Image(type='filepath', label='Image'), 
+                             gr.Slider(label='Minimum Length', minimum=1, maximum=1000, value=30), 
+                             gr.Slider(label='Maximum Length', minimum=1, maximum=1000, value=100)], 
+                     outputs=gr.Textbox(label='Caption'),
+                     theme = gr.themes.Base(primary_hue="teal",secondary_hue="teal",neutral_hue="slate"))
+
+# Add examples
+iface.examples = [
+    ["images/1.png", 30, 100],
+    ["images/2.png", 30, 100],
+    ["images/3.png", 30, 100]
+]
+
 iface.launch()