Update app.py

app.py

@@ -5,19 +5,19 @@ import gradio as gr
 from PIL import Image
 from transformers import pipeline
 
-# Single pipeline: BLIP-2 + Flan-T5-XL for image-to-text
+# Single multi-modal pipeline: BLIP2 + Flan-T5-XL
 pipe = pipeline(
-    "image-to-text",
+    task="image-text-to-text",
     model="Salesforce/blip2-flan-t5-xl",
     tokenizer="Salesforce/blip2-flan-t5-xl",
+    max_new_tokens=500,
     do_sample=True,
     temperature=1.0,
     top_k=50,
-    top_p=0.95,
-    max_new_tokens=512
+    top_p=0.9,
 )
 
-# Hard-coded example-ad URLs
+# Hard-coded gallery URLs
 def get_recommendations():
     return [
         "https://i.imgur.com/InC88PP.jpeg",
@@ -33,11 +33,10 @@ def get_recommendations():
     ]
 
 def process(image: Image):
-    # A single prompt that asks for exactly what you need
     prompt = (
-        "You are a smart ad analyst. Given the following ad image, output:\n"
-        "Category: <one concise label>\n"
-        "Analysis: <exactly five sentences explaining what it communicates and its emotional impact>\n"
+        "You are an expert ad critic. Given the image below, output exactly three sections:\n\n"
+        "Category: <one concise label>\n\n"
+        "Analysis: <exactly five sentences explaining what the ad communicates and its emotional impact>\n\n"
         "Suggestions:\n"
         "- <bullet 1>\n"
         "- <bullet 2>\n"
@@ -45,26 +44,28 @@ def process(image: Image):
         "- <bullet 4>\n"
         "- <bullet 5>\n"
     )
-    raw = pipe(image, prompt=prompt)[0]["generated_text"]
 
-    # Parse out the three sections
-    cat_match = re.search(r"Category:(.*)Analysis:", raw, re.S)
-    ana_match = re.search(r"Analysis:(.*)Suggestions:", raw, re.S)
-    sug_match = re.search(r"Suggestions:(.*)", raw, re.S)
+    # run the multi-modal pipeline
+    result = pipe(image, prompt=prompt)[0]["generated_text"]
 
-    category    = cat_match.group(1).strip() if cat_match else ""
-    analysis    = ana_match.group(1).strip() if ana_match else ""
-    suggestions = sug_match.group(1).strip() if sug_match else ""
+    # extract the three parts via regex
+    cat = re.search(r"Category:(.*?)Analysis:", result, re.S)
+    ana = re.search(r"Analysis:(.*?)Suggestions:", result, re.S)
+    sug = re.search(r"Suggestions:(.*)", result, re.S)
 
-    # Ensure suggestions each start with '-'
-    bullets = [line.strip() for line in suggestions.splitlines() if line.strip()]
+    category    = cat.group(1).strip() if cat else ""
+    analysis    = ana.group(1).strip() if ana else ""
+    suggestions = sug.group(1).strip() if sug else ""
+
+    # ensure exactly five bullets
+    bullets = [line for line in suggestions.splitlines() if line.startswith("-")]
     if len(bullets) < 5:
-        bullets = bullets + ["- (no bullet returned)"] * (5 - len(bullets))
+        bullets += ["- (no suggestion)"] * (5 - len(bullets))
     suggestions = "\n".join(bullets[:5])
 
     return category, analysis, suggestions, get_recommendations()
 
-# Build UI
+# build the Gradio interface
 with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
     gr.Markdown("## 📢 Smart Ad Analyzer")
     gr.Markdown(
@@ -75,10 +76,10 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
     with gr.Row():
         image_input = gr.Image(type="pil", label="Upload Ad Image")
         with gr.Column():
-            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", size="sm", variant="primary")
+            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", size="sm", variant="primary")
 
     gallery = gr.Gallery(label="Recommended Example Ads", show_label=True)