Delete app.py

app.py

@@ -1,32 +0,0 @@
-import gradio as gr
-import requests
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-import time
-
-processor = BlipProcessor.from_pretrained("prithivMLmods/Beetz-Image-Captioning")
-model = BlipForConditionalGeneration.from_pretrained("prithivMLmods/Beetz-Image-Captioning")
-
-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='Beetz-Image-Captioning-Blip', 
-                     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()