Update app.py

app.py

@@ -12,7 +12,7 @@ from transformers import (
 
 DEVICE = 0 if torch.cuda.is_available() else -1
 
-# BLIP captioner setup
+# BLIP captioner
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
 blip_model = AutoModelForVision2Seq.from_pretrained("Salesforce/blip-image-captioning-large")
 caption_pipe = pipeline(
@@ -23,7 +23,7 @@ caption_pipe = pipeline(
     device=DEVICE,
 )
 
-# FLAN-T5 setup
+# FLAN-T5
 FLAN_MODEL = "google/flan-t5-large"
 flan_tokenizer = AutoTokenizer.from_pretrained(FLAN_MODEL)
 flan_model = AutoModelForSeq2SeqLM.from_pretrained(FLAN_MODEL)
@@ -37,6 +37,7 @@ category_pipe = pipeline(
     do_sample=True,
     temperature=1.0,
 )
+
 analysis_pipe = pipeline(
     "text2text-generation",
     model=flan_model,
@@ -46,6 +47,7 @@ analysis_pipe = pipeline(
     do_sample=True,
     temperature=1.0,
 )
+
 suggestion_pipe = pipeline(
     "text2text-generation",
     model=flan_model,
@@ -55,6 +57,7 @@ suggestion_pipe = pipeline(
     do_sample=True,
     temperature=1.0,
 )
+
 expansion_pipe = pipeline(
     "text2text-generation",
     model=flan_model,
@@ -82,70 +85,86 @@ def process(image: Image):
     if image is None:
         return "", "", "", get_recommendations()
 
-    # Captioning with BLIP
+    # BLIP caption
     caption_res = caption_pipe(image, max_new_tokens=64)
     raw_caption = caption_res[0]["generated_text"].strip()
+
+    # Expand if too short
     if len(raw_caption.split()) < 3:
-        desc = expansion_pipe(f"Expand into a detailed description: {raw_caption}")[0]["generated_text"].strip()
+        exp = expansion_pipe(f"Expand into a detailed description: {raw_caption}")
+        desc = exp[0]["generated_text"].strip()
     else:
         desc = raw_caption
 
-    # Ad Category
+    # Category
     cat_prompt = (
         f"Description: {desc}\n\n"
-        "Provide a concise, one or two-word category label for this ad (examples: 'Food', 'Fitness', 'Fashion', 'Tech'):"
+        "Provide a concise category label for this ad (e.g. 'Food', 'Fitness'):"
     )
     cat_out = category_pipe(cat_prompt)[0]["generated_text"].splitlines()[0].strip()
 
-    # Analysis
+    # Five-sentence analysis
     ana_prompt = (
         f"Description: {desc}\n\n"
-        "Write exactly five unique and meaningful sentences explaining what this ad communicates, its visual style, the target audience, the marketing message, and emotional appeal."
+        "Write exactly five sentences explaining what this ad communicates and its emotional impact."
     )
     ana_raw = analysis_pipe(ana_prompt)[0]["generated_text"].strip()
     sentences = re.split(r'(?<=[.!?])\s+', ana_raw)
     analysis = " ".join(sentences[:5])
 
-    # Bullet Suggestions (enforced non-repetitive)
+    # Five bullet-point suggestions, improved filtering
     sug_prompt = (
         f"Description: {desc}\n\n"
-        "Suggest five actionable and distinct ways this ad could be improved. Each improvement must start with '- ' and each one should address a different aspect such as clarity, visual design, call-to-action, message, or emotional engagement. No repeats or generic phrases."
+        "Provide five unique, actionable improvement suggestions for this ad. "
+        "Each suggestion must begin with '- ' and each one should cover a different topic, such as clarity, visual design, call-to-action, audience targeting, or emotional impact. "
+        "Do not repeat ideas or use generic language. Each suggestion should be specific and creative."
     )
     sug_raw = suggestion_pipe(sug_prompt)[0]["generated_text"].strip()
-    bullets = [l for l in sug_raw.splitlines() if l.strip().startswith("- ")]
-    seen = set()
-    cleaned = []
-    for b in bullets:
-        text = b.strip().lower()
-        if text not in seen and len(cleaned) < 5:
-            cleaned.append(b)
-            seen.add(text)
-    while len(cleaned) < 5:
-        cleaned.append("- Add more product context or a stronger call to action.")
-
-    suggestions = "\n".join(cleaned[:5])
+    lines = [l.strip() for l in sug_raw.splitlines() if l.strip().startswith("-")]
+    used = set()
+    unique_lines = []
+    for l in lines:
+        key = l.lower().strip("- .")
+        if key not in used and len(unique_lines) < 5:
+            unique_lines.append(l)
+            used.add(key)
+    fallbacks = [
+        "- Use a bolder headline or color contrast to capture attention.",
+        "- Make the product or offer more visually prominent.",
+        "- Add a clearer call to action, such as 'Order Now' or 'Learn More'.",
+        "- Tailor the messaging to a more specific audience segment.",
+        "- Add emotional or humorous elements to boost engagement."
+    ]
+    for fb in fallbacks:
+        if len(unique_lines) >= 5:
+            break
+        if fb.lower() not in used:
+            unique_lines.append(fb)
+            used.add(fb.lower())
+    suggestions = "\n".join(unique_lines[:5])
 
     return cat_out, analysis, suggestions, get_recommendations()
 
 def main():
     with gr.Blocks(title="Smart Ad Analyzer") as demo:
-        gr.Markdown("## 📢 Smart Ad Analyzer")
+        gr.Markdown("# 📢 Smart Ad Analyzer")
         gr.Markdown(
-            "Welcome to Smart Ad Analyzer! \n\n"
-            "Upload any ad image and instantly receive:\n"
-            "- 📂 **Ad Category:** How would an AI classify this ad?\n"
-            "- 📊 **In-depth Analysis:** Five unique sentences covering message, design, audience, and emotion.\n"
-            "- 🚀 **Improvement Suggestions:** Five non-repetitive, actionable ideas to make your ad stronger.\n"
-            "- 📸 **Example Ads Gallery:** For creative inspiration.\n\n"
-            "Designed for marketers, designers, students, or anyone curious about effective advertising. No account or API needed."
+            """
+            Upload an ad image to instantly get:
+            -  **Ad Category**: A clear and relevant label for the type of ad.
+            -  **Five-sentence Analysis**: A comprehensive explanation of the ad's message, design, and emotional impact.
+            -  **Five Improvement Suggestions**: Specific, unique tips to boost ad performance.
+            -  **Example Ads**: Inspiring ad visuals for creative reference.
+            
+            This tool helps marketers, designers, and founders strengthen any ad campaign in seconds.
+            """
         )
         with gr.Row():
             inp = gr.Image(type='pil', label='Upload Ad Image')
             with gr.Column():
-                # BLIP caption hidden from UI
                 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)
+                sug_out = gr.Textbox(label='🚀 Improvement Suggestions', lines=8, interactive=False)
                 btn = gr.Button('Analyze Ad', variant='primary')
         gallery = gr.Gallery(label='Example Ads')
         btn.click(