Update app.py

app.py

@@ -10,10 +10,10 @@ from transformers import (
     AutoModelForSeq2SeqLM,
 )
 
-# Device config
+# Auto-detect CPU/GPU
 DEVICE = 0 if torch.cuda.is_available() else -1
 
-# BLIP image captioning
+# 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(
@@ -24,7 +24,7 @@ caption_pipe = pipeline(
     device=DEVICE,
 )
 
-# FLAN-T5 for text-to-text
+# 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)
@@ -68,8 +68,8 @@ expansion_pipe = pipeline(
     do_sample=False,
 )
 
-# Example gallery helper
 def get_recommendations():
+    # Returns list of 10 example ad image URLs
     return [
         "https://i.imgur.com/InC88PP.jpeg",
         "https://i.imgur.com/7BHfv4T.png",
@@ -83,30 +83,29 @@ def get_recommendations():
         "https://i.imgur.com/Xj92Cjv.jpeg",
     ]
 
-# Main processing function
 def process(image: Image):
     if image is None:
         return "", "", "", get_recommendations()
 
-    # 1) BLIP caption
+    # 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
+    # 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
+    # 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
+    # 3. Five-sentence analysis
     ana_prompt = (
         f"Description: {desc}\n\n"
         "Write exactly five sentences explaining what this ad communicates and its emotional impact."
@@ -115,45 +114,58 @@ def process(image: Image):
     sentences = re.split(r'(?<=[.!?])\s+', ana_raw)
     analysis = " ".join(sentences[:5])
 
-    # 4) Five bullet-point suggestions, deduplicate
+    # 4. Five bullet-point suggestions (unique only)
     sug_prompt = (
         f"Description: {desc}\n\n"
-        "Suggest five unique, practical improvements for this ad, each starting with '- '. Each must address a different aspect (such as message, visuals, CTA, targeting, layout, or design). Avoid repeating the same suggestion."
+        "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."
     )
     sug_raw = suggestion_pipe(sug_prompt)[0]["generated_text"].strip()
-    # Only keep unique, non-empty suggestions
     bullets = []
     seen = set()
     for line in sug_raw.splitlines():
         if line.startswith("-"):
-            item = line.strip()
-            # Remove exact duplicates
-            if item not in seen and len(bullets) < 5:
-                bullets.append(item)
-                seen.add(item)
-        elif line.strip() and len(bullets) < 5:
-            item = "- " + line.strip()
-            if item not in seen:
-                bullets.append(item)
-                seen.add(item)
-    while len(bullets) < 5:
-        bullets.append(f"- Add a new visual or messaging element for more impact.")
+            suggestion = line.strip()
+            # Remove duplicates and ignore empty lines
+            if suggestion and suggestion not in seen:
+                bullets.append(suggestion)
+                seen.add(suggestion)
+        elif line.strip():
+            suggestion = "- " + line.strip()
+            if suggestion and suggestion not in seen:
+                bullets.append(suggestion)
+                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.",
+        "- Use contrasting colors for better readability.",
+        "- Highlight the unique selling point of the product.",
+        "- Simplify the design to reduce clutter."
+    ]
+    for default in defaults:
+        if len(bullets) < 5 and default not in seen:
+            bullets.append(default)
     suggestions = "\n".join(bullets[:5])
 
     return cat_out, analysis, suggestions, get_recommendations()
 
-# Gradio UI
 def main():
     with gr.Blocks(title="Smart Ad Analyzer") as demo:
         gr.Markdown("## 📢 Smart Ad Analyzer")
         gr.Markdown(
-            "Welcome to the Smart Ad Analyzer! Upload any advertisement image to receive instant, AI-powered marketing feedback.\n\n"
-            "What you'll get:\n"
-            "- **Ad Category:** What type of product or service is advertised?\n"
-            "- **Detailed Analysis:** A five-sentence summary explaining the ad's message, design, and emotional effect.\n"
-            "- **Improvement Suggestions:** Five unique, actionable tips to boost ad performance (not just generic advice!).\n"
-            "- **Example Gallery:** See other effective ad examples for inspiration.\n\n"
-            "Ideal for marketers, entrepreneurs, students, or anyone curious about what makes a great ad!"
+            """
+            **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.
+
+            Perfect for marketers, founders, designers, and anyone looking to boost ad performance with actionable insights!
+            """
         )
         with gr.Row():
             inp = gr.Image(type='pil', label='Upload Ad Image')