Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration, AutoTokenizer
+import itertools
+from nltk.corpus import stopwords
+import nltk
+import easyocr
+import torch
+import numpy as np
+nltk.download('stopwords')
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+reader = easyocr.Reader(['en'])
+# set up Streamlit app
+st.set_page_config(layout='wide', page_title='Image Hashtag Recommender')
+
+def genrate_caption(image_file):
+    image = Image.open(image_file).convert('RGB')
+    inputs = processor(image, return_tensors="pt")
+    output_ids = model.generate(**inputs)
+    output_text = processor.decode(output_ids[0], skip_special_tokens=True)
+    return output_text
+    
+st.title("Image Caption and HashTag Recommender")
+image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if image_file is not None:
+    try:
+        caption = genrate_caption(image_file)
+        if len(caption) > 0:
+            st.write(f"Caption : {caption}")
+        
+        else:
+            st.write("No caption found for this image.")
+    except Exception as e:
+        st.write(f"Error: {e}")
+
+