Duplicate from vama09/HashtagAndCaption

Co-authored-by: Varun <[email protected]>

.gitattributes

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README.md

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+---
+title: HashtagAndCaption
+emoji: 🏃
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.19.0
+app_file: app.py
+pinned: false
+duplicated_from: vama09/HashtagAndCaption
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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')
+
+# load the model and tokenizer
+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 generate_hashtags(image_file):
+    # get image and convert to RGB mode
+    image = Image.open(image_file).convert('RGB')
+
+    # extract image features
+    inputs = processor(image, return_tensors="pt")
+
+    output_ids = model.generate(**inputs)
+    # out_text = processor.decode(out[0], skip_special_tokens=True)
+
+    # decode the model output to text and extract caption words
+    output_text = processor.decode(output_ids[0], skip_special_tokens=True)
+    caption_words = [word.lower() for word in output_text.split() if not word.startswith("#")]
+
+    # remove stop words from caption words
+    stop_words = set(stopwords.words('english'))
+    caption_words = [word for word in caption_words if word not in stop_words]
+
+    # use easyocr to extract text from the image
+    text = reader.readtext(np.array(image))
+    detected_text = " ".join([item[1] for item in text])
+
+    # combine caption words and detected text
+    all_words = caption_words + detected_text.split()
+
+    # generate combinations of words for hashtags
+    hashtags = []
+    for n in range(1, 4):
+        word_combinations = list(itertools.combinations(all_words, n))
+        for combination in word_combinations:
+            hashtag = "#" + "".join(combination)
+            hashtags.append(hashtag)
+
+    # return top 10 hashtags by frequency
+    top_hashtags = [tag for tag in sorted(set(hashtags), key=hashtags.count, reverse=True) if tag != "#"]
+    return [top_hashtags[:10], output_text]
+
+st.title("Image Caption and HashTag Recommender")
+
+image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+# if the user has submitted an image, generate hashtags
+if image_file is not None:
+    try:
+        hashtags = generate_hashtags(image_file)
+        if len(hashtags) > 0:
+            st.write(f"Caption : {hashtags[1]}")
+            st.write("Top 10 hashtags for this image:")
+            for tag in hashtags[0]:
+                st.write(tag)
+        else:
+            st.write("No hashtags found for this image.")
+    except Exception as e:
+        st.write(f"Error: {e}")
+

requirements.txt

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+easyocr==1.6.2
+nltk==3.7
+numpy==1.23.5
+Pillow==9.5.0
+streamlit==1.21.0
+torch==2.0.0
+transformers==4.28.1