Update app.py

app.py

@@ -1,3 +1,50 @@
 import gradio as gr
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+import re 
+import string
+import nltk
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+def hashtag_generator(image):
+     raw_image = Image.fromarray(image).convert('RGB')
+     inputs = processor(raw_image, return_tensors="pt")
+     out = model.generate(
+            **inputs,
+            num_return_sequences=4, 
+            max_length=32, 
+            early_stopping=True, 
+            num_beams=4, 
+            no_repeat_ngram_size=2, 
+            length_penalty=0.8 
+             )
+     captions = ""
+     for i, caption in enumerate(out):
+            captions = captions  +processor.decode(caption, skip_special_tokens=True) + " ,"
+
+     text = "".join([word.lower() for word in captions if word not in string.punctuation])
+     tokens = re.split('\W+', text)
+     text = [wn.lemmatize(word) for word in tokens if word not in stopwords]
+     words = set(text)
+     hashtags = ""
+     for hashtag in words:
+         if len(hashtag) == 0:
+             pass
+         else:
+             hashtags = hashtags + f" ,#{hashtag}"
+     return hashtags[2:]
+
+gr.Interface(hashtag_generator, inputs= gr.inputs.Image(), outputs = gr.outputs.Textbox(), live = True).launch()
+
+
+     
+     
+
+    
+
 
-gr.Interface.load("models/Salesforce/blip-image-captioning-base").launch()