Create app.py

app.py

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+import gradio as gr
+#import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+#num_captions = gr.Dropdown([1, 2, 3, 4,5],label = "select no.of captions to generate")
+
+def caption_generator(image, num_captions):
+     num_captions = int(float(num_captions))
+     raw_image = Image.fromarray(image).convert('RGB')
+     inputs = processor(raw_image, return_tensors="pt")
+     out = model.generate(
+            **inputs,
+            num_return_sequences=num_captions, # generate 3 captions
+            max_length=32, # maximum length of generated captions
+            early_stopping=True, # stop generating captions when all beam hypotheses have finished
+            num_beams=num_captions, # number of beams for beam search
+            no_repeat_ngram_size=2, # avoid repeating n-grams of size 2 or larger
+            length_penalty=0.8 # higher penalty value will encourage shorter captions
+             )
+     captions = ""
+     for i, caption in enumerate(out):
+            captions = captions  +processor.decode(caption, skip_special_tokens=True) + " ,"
+     return captions 
+
+gr.Interface(caption_generator, inputs= [gr.inputs.Image(), gr.Dropdown([1, 2, 3, 4,5],value = [2], label = "select no.of captions to generate")], outputs = gr.outputs.Textbox(), live = True).launch()