Update app.py

app.py

@@ -10,9 +10,10 @@ from transformers import (
     AutoModelForSeq2SeqLM,
 )
 
+# Auto-detect CPU/GPU
 DEVICE = 0 if torch.cuda.is_available() else -1
 
-
+# Load BLIP captioning model
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
 blip_model = AutoModelForVision2Seq.from_pretrained("Salesforce/blip-image-captioning-large")
 caption_pipe = pipeline(
@@ -23,7 +24,7 @@ caption_pipe = pipeline(
     device=DEVICE,
 )
 
-
+# Load Flan-T5 for text-to-text
 FLAN_MODEL = "google/flan-t5-large"
 flan_tokenizer = AutoTokenizer.from_pretrained(FLAN_MODEL)
 flan_model = AutoModelForSeq2SeqLM.from_pretrained(FLAN_MODEL)
@@ -68,7 +69,7 @@ expansion_pipe = pipeline(
 )
 
 def get_recommendations():
-   
+    # Returns list of 10 example ad image URLs
     return [
         "https://i.imgur.com/InC88PP.jpeg",
         "https://i.imgur.com/7BHfv4T.png",
@@ -86,25 +87,25 @@ def process(image: Image):
     if image is None:
         return "", "", "", get_recommendations()
 
-    
+    # 1. BLIP caption
     caption_res = caption_pipe(image, max_new_tokens=64)
     raw_caption = caption_res[0]["generated_text"].strip()
 
-    
+    # 1a. Expand caption if too short
     if len(raw_caption.split()) < 3:
         exp = expansion_pipe(f"Expand into a detailed description: {raw_caption}")
         desc = exp[0]["generated_text"].strip()
     else:
         desc = raw_caption
 
-    
+    # 2. Category
     cat_prompt = (
         f"Description: {desc}\n\n"
         "Provide a concise category label for this ad (e.g. 'Food', 'Fitness'):"
     )
     cat_out = category_pipe(cat_prompt)[0]["generated_text"].splitlines()[0].strip()
 
-  
+    # 3. Five-sentence analysis
     ana_prompt = (
         f"Description: {desc}\n\n"
         "Write exactly five sentences explaining what this ad communicates and its emotional impact."
@@ -113,7 +114,7 @@ def process(image: Image):
     sentences = re.split(r'(?<=[.!?])\s+', ana_raw)
     analysis = " ".join(sentences[:5])
 
-   
+    # 4. Five bullet-point suggestions (unique only)
     sug_prompt = (
         f"Description: {desc}\n\n"
         "Suggest five unique, practical improvements for this ad. Each must address a different aspect (message, visuals, call-to-action, targeting, layout, or design). Each suggestion must be one sentence and start with '- '. Do NOT repeat suggestions."
@@ -135,7 +136,7 @@ def process(image: Image):
                 seen.add(suggestion)
         if len(bullets) == 5:
             break
-    
+    # Add non-repetitive defaults if needed
     defaults = [
         "- Make the main headline more eye-catching.",
         "- Add a clear and visible call-to-action button.",
@@ -158,10 +159,10 @@ def main():
             **Upload your ad image below and instantly get expert feedback.**
 
             This AI tool will analyze your ad and provide:
-            -  **Category** — What type of ad is this?
-            -  **In-depth Analysis** — Five detailed sentences covering message, visuals, emotional impact, and more.
-            -  **Improvement Suggestions** — Five actionable, unique ways to make your ad better.
-            -  **Inspiration Gallery** — See other effective ads for ideas.
+            - 📂 **Category** — What type of ad is this?
+            - 📊 **In-depth Analysis** — Five detailed sentences covering message, visuals, emotional impact, and more.
+            - 🚀 **Improvement Suggestions** — Five actionable, unique ways to make your ad better.
+            - 📸 **Inspiration Gallery** — See other effective ads for ideas.
 
             Perfect for marketers, founders, designers, and anyone looking to boost ad performance with actionable insights!
             """
@@ -169,9 +170,9 @@ def main():
         with gr.Row():
             inp = gr.Image(type='pil', label='Upload Ad Image')
             with gr.Column():
-                cat_out = gr.Textbox(label=' Ad Category', interactive=False)
-                ana_out = gr.Textbox(label=' Ad Analysis', lines=5, interactive=False)
-                sug_out = gr.Textbox(label=' Improvement Suggestions', lines=5, interactive=False)
+                cat_out = gr.Textbox(label='📂 Ad Category', interactive=False)
+                ana_out = gr.Textbox(label='📊 Ad Analysis', lines=5, interactive=False)
+                sug_out = gr.Textbox(label='🚀 Improvement Suggestions', lines=5, interactive=False)
                 btn = gr.Button('Analyze Ad', variant='primary')
         gallery = gr.Gallery(label='Example Ads')
         btn.click(