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app.py

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+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='Blip Image Captioning Large', 
+                     description="[Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large)",
+                     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"),)
+iface.launch()

requirements.txt

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+transformers
+torch