Update app.py

app.py

@@ -1,3 +1,4 @@
+# Import all necessary libraries and don't forget to check out Dependencies
 import streamlit as st
 from PIL import Image
 import numpy as np
@@ -14,14 +15,12 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
 
-# Directory path to the saved model on Google Drive
+# Load the model-pretrained
 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")
 
-
+# Function to generate captions
 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])
@@ -30,7 +29,7 @@ def generate_captions(image):
     generated_caption = sentence.replace(text_to_remove, "")
     return generated_caption
 
-# use easyocr to extract text from the image
+#  kinda-Function easyocr to extract text from the image
 def image_text(image):
     img_np = np.array(image)
     reader = easyocr.Reader(['en'])
@@ -47,17 +46,13 @@ 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
 
+# function to get and add trending 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()
 
@@ -90,21 +85,17 @@ def trending_hashtags(caption):
   # 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
+  # Select top k relevant hashtags (e.g., top 5) without duplicates and return them
   selected_hashtags = list(set(sorted_hashtags[:5]))
-
   selected_hashtag = [word.strip("'") for word in selected_hashtags]
-
   return selected_hashtag
 
-# create the Streamlit app
+# Streamlit app Creation
 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.')
-
+    st.title('Have a :green[Bueatiful pic!] Looking for :orange[Trending Hashtags to post it on your social handle?]. Here is some Help')
+    
     # create file uploader
-    uploaded_file = st.file_uploader("Got You Covered, Upload your wish!, magic on the Way! ", type=["jpg", "jpeg", "png"])
+    uploaded_file = st.file_uploader("Upload Picture of your wish!, :violet[magic on the Way! ]", type=["jpg", "jpeg", "png"])
 
     # check if file has been uploaded
     if uploaded_file is not None:
@@ -122,19 +113,15 @@ def app():
 
         #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)
+        st.write("Magical hashies have arrived* :sparkles: ", combined_hashtags)
 
 # run the app
 if __name__ == '__main__':