Update app.py

app.py

@@ -17,7 +17,6 @@ import spacy
 
 import re
 
-
 # Initialize the processor and model for the large COCO model
 processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
 model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
@@ -83,7 +82,6 @@ def extract_main_words(text):
     main_words = [token.lemma_ for token in doc if token.pos_ == 'NOUN']
     return main_words
 
-
 def get_topics(text):
     # Vectorize the text
     vectorizer = CountVectorizer()
@@ -109,8 +107,6 @@ def compute_similarity(caption1, caption2):
     similarity_score = cosine_sim[0, 1]
     return similarity_score
 
-
-# Cell 3
 def evaluate_caption(image, caption1, caption2, unique_refined_labels):
     # Scores initialization
     score_caption1 = 0
@@ -120,7 +116,6 @@ def evaluate_caption(image, caption1, caption2, unique_refined_labels):
     object_presence_score1 = 0
     object_presence_score2 = 0
 
-
      # Assume you have a function to extract main words
     main_words_caption1 = extract_main_words(caption1)
     main_words_caption2 = extract_main_words(caption2)
@@ -215,14 +210,13 @@ def process_image(image_path):
     # evealuate the captions
     better_caption = evaluate_caption(image, caption1, caption2, unique_refined_labels)
 
-
     return caption1, caption2, better_caption
 
 import gradio as gr
 
 img_cap_ui = gr.Interface(
     fn=process_image,
-    title="Image Captioning with Automactic Evaluation",
+    title="Image Captioning with Automatic Evaluation",
     description="Caution: this is a research experiment for personal use, please review the captions before using.",
     inputs=gr.inputs.Image(type="filepath",label="Add your image"),
     outputs=[gr.Textbox(label="Caption from the git-coco model", show_copy_button=True),
@@ -234,6 +228,3 @@ img_cap_ui = gr.Interface(
 )
 
 img_cap_ui.launch()
-
-
-