Update app.py

app.py

@@ -12,7 +12,7 @@ from transformers import (
 
 DEVICE = 0 if torch.cuda.is_available() else -1
 
-# BLIP captioner
+# BLIP
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
 blip_model = AutoModelForVision2Seq.from_pretrained("Salesforce/blip-image-captioning-large")
 caption_pipe = pipeline(
@@ -23,7 +23,6 @@ caption_pipe = pipeline(
     device=DEVICE,
 )
 
-# FLAN-T5
 FLAN_MODEL = "google/flan-t5-large"
 flan_tokenizer = AutoTokenizer.from_pretrained(FLAN_MODEL)
 flan_model = AutoModelForSeq2SeqLM.from_pretrained(FLAN_MODEL)
@@ -37,7 +36,6 @@ category_pipe = pipeline(
     do_sample=True,
     temperature=1.0,
 )
-
 analysis_pipe = pipeline(
     "text2text-generation",
     model=flan_model,
@@ -47,7 +45,6 @@ analysis_pipe = pipeline(
     do_sample=True,
     temperature=1.0,
 )
-
 suggestion_pipe = pipeline(
     "text2text-generation",
     model=flan_model,
@@ -57,7 +54,6 @@ suggestion_pipe = pipeline(
     do_sample=True,
     temperature=1.0,
 )
-
 expansion_pipe = pipeline(
     "text2text-generation",
     model=flan_model,
@@ -83,27 +79,19 @@ def get_recommendations():
 
 def process(image: Image):
     if image is None:
-        return "", "", "", get_recommendations()
-
-    # BLIP caption
+        return "", "", "", "", get_recommendations()
     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:
         exp = expansion_pipe(f"Expand into a detailed description: {raw_caption}")
         desc = exp[0]["generated_text"].strip()
     else:
         desc = raw_caption
-
-    # 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
     ana_prompt = (
         f"Description: {desc}\n\n"
         "Write exactly five sentences explaining what this ad communicates and its emotional impact."
@@ -111,57 +99,58 @@ def process(image: Image):
     ana_raw = analysis_pipe(ana_prompt)[0]["generated_text"].strip()
     sentences = re.split(r'(?<=[.!?])\s+', ana_raw)
     analysis = " ".join(sentences[:5])
-
-    # Five bullet-point suggestions, improved filtering
     sug_prompt = (
         f"Description: {desc}\n\n"
-        "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."
+        "Suggest five **different** and actionable improvements for this ad. "
+        "Each must start with '- ' and be a single sentence. "
+        "Avoid repeating any idea or wording."
     )
     sug_raw = suggestion_pipe(sug_prompt)[0]["generated_text"].strip()
-    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."
+    seen = set()
+    bullets = []
+    for l in sug_raw.splitlines():
+        if l.startswith("-"):
+            key = l[2:].strip().lower()
+            if key and key not in seen:
+                seen.add(key)
+                bullets.append(l.strip())
+        if len(bullets) == 5:
+            break
+    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 fallbacks:
-        if len(unique_lines) >= 5:
+    for fb in fallback:
+        if len(bullets) == 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()
+        fb_key = fb[2:].strip().lower()
+        if fb_key not in seen:
+            bullets.append(fb)
+            seen.add(fb_key)
+    suggestions = "\n".join(bullets[: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(
             """
-            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.
+            Upload an ad image and get AI-powered creative feedback instantly:
+            -  **Ad Category** (concise and relevant)
+            -  **Five-sentence Analysis** (ad message, design, impact)
+            -  **Five unique Improvement Suggestions**
+            -  **Example Ads** for inspiration
+
+            Get quick, actionable advice for better ads—no creative block, no guesswork.
             """
         )
         with gr.Row():
             inp = gr.Image(type='pil', label='Upload Ad Image')
             with gr.Column():
+                # BLIP caption hidden!
                 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=8, interactive=False)