Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import nltk
+nltk.download('stopwords')
+nltk.download('punkt')
+import pandas as pd
+import random
+import easyocr
+import re
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from transformers import AutoTokenizer, VisionEncoderDecoderModel, ViTFeatureExtractor
+
+# Directory path to the saved model on Google Drive
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+# Load the feature extractor and tokenizer
+feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+
+def generate_captions(image):
+    image = Image.open(image).convert("RGB")
+    generated_caption = tokenizer.decode(model.generate(feature_extractor(image, return_tensors="pt").pixel_values.to("cpu"))[0])
+    sentence = generated_caption
+    text_to_remove = "<|endoftext|>"
+    generated_caption = sentence.replace(text_to_remove, "")
+    return generated_caption
+
+# use easyocr to extract text from the image
+def image_text(image):
+    img_np = np.array(image)
+    reader = easyocr.Reader(['en'])
+    text = reader.readtext(img_np)
+    detected_text = " ".join([item[1] for item in text])
+
+    # Extract individual words, convert to lowercase, and add "#" symbol
+    detected_text= ['#' + entry[1].strip().lower().replace(" ", "_") for entry in text]
+    return detected_text
+
+# Load NLTK stopwords for filtering
+stop_words = set(stopwords.words('english'))
+
+# Add hashtags to keywords, which have been generated from image captioing
+def add_hashtags(keywords):
+    hashtags = []
+    
+    for keyword in keywords:
+        # Generate hashtag from the keyword (you can modify this part as per your requirements)
+        hashtag = '#' + keyword.lower()
+        
+        hashtags.append(hashtag)
+    
+    return hashtags
+
+def trending_hashtags(caption):
+  # Read trending hashtags from a file separated by commas
+  with open("hashies.txt", "r") as file:
+      hashtags_string = file.read()
+
+  # Split the hashtags by commas and remove any leading/trailing spaces
+  trending_hashtags = [hashtag.strip() for hashtag in hashtags_string.split(',')]
+
+  # Create a DataFrame from the hashtags
+  df = pd.DataFrame(trending_hashtags, columns=["Hashtags"])
+
+  # Function to extract keywords from a given text
+  def extract_keywords(caption):
+      tokens = word_tokenize(caption)
+      keywords = [token.lower() for token in tokens if token.lower() not in stop_words]
+      return keywords
+
+  # Extract keywords from caption and trending hashtags
+  caption_keywords = extract_keywords(caption)
+  hashtag_keywords = [extract_keywords(hashtag) for hashtag in df["Hashtags"]]
+
+  # Function to calculate cosine similarity between two strings
+  def calculate_similarity(text1, text2):
+      tfidf_vectorizer = TfidfVectorizer()
+      tfidf_matrix = tfidf_vectorizer.fit_transform([text1, text2])
+      similarity_matrix = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])
+      return similarity_matrix[0][0]
+
+  # Calculate similarity between caption and each trending hashtag
+  similarities = [calculate_similarity(' '.join(caption_keywords), ' '.join(keywords)) for keywords in hashtag_keywords]
+
+  # Sort trending hashtags based on similarity in descending order
+  sorted_hashtags = [hashtag for _, hashtag in sorted(zip(similarities, df["Hashtags"]), reverse=True)]
+
+  # Select top k relevant hashtags (e.g., top 5) without duplicates
+  selected_hashtags = list(set(sorted_hashtags[:5]))
+
+  selected_hashtag = [word.strip("'") for word in selected_hashtags]
+
+  return selected_hashtag
+
+# create the Streamlit app
+def app():
+    st.title('Image from your Side, Trending Hashtags from our Side')
+
+    st.write('Upload an image to see what we have in store.')
+
+    # create file uploader
+    uploaded_file = st.file_uploader("Got You Covered, Upload your wish!, magic on the Way! ", type=["jpg", "jpeg", "png"])
+
+    # check if file has been uploaded
+    if uploaded_file is not None:
+        # load the image
+        image = Image.open(uploaded_file).convert("RGB")
+
+        # Image Captions
+        string = generate_captions(uploaded_file)
+        tokens = word_tokenize(string)
+        keywords = [token.lower() for token in tokens if token.lower() not in stop_words]
+        hashtags = add_hashtags(keywords)
+
+        # Text Captions from image
+        extracted_text = image_text(image)
+
+        #Final Hashtags Generation
+        web_hashtags = trending_hashtags(string)
+
+        combined_hashtags = hashtags + extracted_text + web_hashtags
+
+        # Shuffle the list randomly
+        random.shuffle(combined_hashtags)
+
+        combined_hashtags = list(set(item for item in combined_hashtags[:15] if not re.search(r'\d$', item)))
+
+
+        # display the image
+        st.image(image, caption='The Uploaded File')
+        st.write("First is first captions for your Photo : ", string)
+        st.write("Magical hashies have arrived : ", combined_hashtags)
+
+# run the app
+if __name__ == '__main__':
+    app()