Update app.py

app.py

@@ -10,10 +10,10 @@ from transformers import (
     AutoModelForSeq2SeqLM,
 )
 
-# Auto-detect CPU/GPU
+# Device config
 DEVICE = 0 if torch.cuda.is_available() else -1
 
-# BLIP captioner
+# BLIP image captioning
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
 blip_model = AutoModelForVision2Seq.from_pretrained("Salesforce/blip-image-captioning-large")
 caption_pipe = pipeline(
@@ -68,6 +68,7 @@ expansion_pipe = pipeline(
     do_sample=False,
 )
 
+# Example gallery helper
 def get_recommendations():
     return [
         "https://i.imgur.com/InC88PP.jpeg",
@@ -82,29 +83,30 @@ def get_recommendations():
         "https://i.imgur.com/Xj92Cjv.jpeg",
     ]
 
+# Main processing function
 def process(image: Image):
     if image is None:
-        return "", "", "", "", get_recommendations()
+        return "", "", "", get_recommendations()
 
-    # BLIP caption
+    # 1) BLIP caption
     caption_res = caption_pipe(image, max_new_tokens=64)
     raw_caption = caption_res[0]["generated_text"].strip()
 
-    # Expand 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
 
-    # 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()
 
-    # 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."
@@ -113,67 +115,58 @@ def process(image: Image):
     sentences = re.split(r'(?<=[.!?])\s+', ana_raw)
     analysis = " ".join(sentences[:5])
 
-    # Five bullet-point suggestions - unique, high-quality
+    # 4) Five bullet-point suggestions, deduplicate
     sug_prompt = (
         f"Description: {desc}\n\n"
-        "Provide five distinct improvement suggestions for this ad. Each must start with '- ', be one sentence, and not repeat the same idea."
+        "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."
     )
     sug_raw = suggestion_pipe(sug_prompt)[0]["generated_text"].strip()
+    # Only keep unique, non-empty suggestions
     bullets = []
     seen = set()
-    for l in sug_raw.splitlines():
-        line = l.strip()
+    for line in sug_raw.splitlines():
         if line.startswith("-"):
-            key = line[2:].lower()
-            if key not in seen and len(key) > 5:
-                bullets.append(line)
-                seen.add(key)
-        if len(bullets) == 5:
-            break
-    # Fallbacks
-    fallback = [
-        "- Add a bold and visible call-to-action button.",
-        "- Use brighter colors or higher contrast for more visual impact.",
-        "- Refine the text for greater clarity and conciseness.",
-        "- Adjust the image layout for better balance and focus.",
-        "- Highlight product benefits more clearly in the headline."
-    ]
-    for fb in fallback:
-        if len(bullets) == 5:
-            break
-        fb_key = fb[2:].lower()
-        if fb_key not in seen:
-            bullets.append(fb)
-            seen.add(fb_key)
+            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.")
     suggestions = "\n".join(bullets[:5])
 
-    return "", cat_out, analysis, suggestions, get_recommendations()  # Hides BLIP Caption by returning blank
+    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(
-            "##  Smart Ad Analyzer\n"
-            "Upload any advertisement image below and get an instant breakdown:\n\n"
-            "- **Ad Category:** Instantly identifies what sector your ad fits into\n"
-            "- **Ad Analysis:** Five concise sentences explaining what your ad communicates and the emotional response it targets\n"
-            "- **Improvement Suggestions:** Five practical, unique tips to make your ad more effective\n"
-            "- **Example Ads Gallery:** See proven ad designs for inspiration\n\n"
-            "_Ideal for marketers, business owners, students, and creative teams who want to boost ad impact using AI. No technical skill required._"
+            "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!"
         )
         with gr.Row():
             inp = gr.Image(type='pil', label='Upload Ad Image')
             with gr.Column():
-                # To hide BLIP caption, don't add the textbox output
-                # cap_out = gr.Textbox(label=' BLIP Caption', interactive=False, visible=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)
+                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(
             fn=process,
             inputs=[inp],
-            outputs=["textbox", cat_out, ana_out, sug_out, gallery],  # BLIP output is blank, textbox hidden
+            outputs=[cat_out, ana_out, sug_out, gallery],
         )
         gr.Markdown('Made by Simon Thalmay')
     return demo