Update app.py

app.py

@@ -49,7 +49,7 @@ analysis_pipe = pipeline(
     temperature=1.0,
 )
 
-# Set higher temperature for more variety
+# Higher temp for more variety in suggestions
 suggestion_pipe = pipeline(
     "text2text-generation",
     model=flan_model,
@@ -57,7 +57,7 @@ suggestion_pipe = pipeline(
     device=DEVICE,
     max_new_tokens=256,
     do_sample=True,
-    temperature=1.2,
+    temperature=1.3,
 )
 
 expansion_pipe = pipeline(
@@ -70,7 +70,6 @@ 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",
@@ -88,38 +87,29 @@ def process(image: Image):
     if image is None:
         return "", "", "", get_recommendations()
 
-    # 1. BLIP caption
+    # BLIP caption
     caption_res = caption_pipe(image, max_new_tokens=64)
     raw_caption = caption_res[0]["generated_text"].strip()
+    desc = raw_caption if len(raw_caption.split()) >= 3 else expansion_pipe(f"Expand into a detailed description: {raw_caption}")[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'):"
-    )
+    # Category
+    cat_prompt = f"Description: {desc}\n\nProvide 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
+    # Five-sentence analysis
     ana_prompt = (
         f"Description: {desc}\n\n"
         "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])
+    analysis = " ".join(sentences[:5]).strip()
 
-    # 4. Five bullet-point suggestions (uniqueness enforced)
+    # **KEY CHANGE**: Use analysis in suggestion prompt!
     sug_prompt = (
-        f"Description: {desc}\n\n"
-        "Suggest five ways this ad could be improved. Each suggestion must be about a different aspect, such as visuals, message, call-to-action, color, clarity, layout, or audience targeting. "
-        "Each suggestion must start with '- ' and be one full sentence. Make sure each is different from the others."
+        f"Ad description: {desc}\n"
+        f"Ad analysis: {analysis}\n\n"
+        "Based on this, suggest five unique and specific improvements for this ad. Each suggestion should be one clear sentence starting with '- ' and focus on a different aspect, like message, visuals, call-to-action, color, clarity, layout, targeting, or emotional impact. Do NOT repeat suggestions."
     )
     sug_raw = suggestion_pipe(sug_prompt)[0]["generated_text"].strip()
     all_sugs = [line.strip() for line in sug_raw.splitlines() if line.strip().startswith("-")]
@@ -133,7 +123,7 @@ def process(image: Image):
         if len(unique_sugs) == 5:
             break
 
-    # Add non-repetitive defaults if needed
+    # Add defaults if needed
     defaults = [
         "- Make the main headline more eye-catching.",
         "- Add a clear and visible call-to-action button.",