Update app.py

app.py

@@ -1,18 +1,18 @@
 import gradio as gr
 from PIL import Image
-import io
 import os
+import io
 from huggingface_hub import InferenceClient
 
-# Authenticate (Space secrets or env variable)
-HF_TOKEN = os.environ.get("HF_TOKEN")
+HF_TOKEN = os.environ.get("HF_TOKEN")  # For Hugging Face Spaces
+
 client = InferenceClient(
-    repo_id="google/gemma-3-4b-it",
+    model="google/gemma-3-4b-it",
     token=HF_TOKEN
 )
 
 def get_recommendations():
-    # Example ad image URLs for the gallery
+    # Returns list of 10 example ad image URLs
     return [
         "https://i.imgur.com/InC88PP.jpeg",
         "https://i.imgur.com/7BHfv4T.png",
@@ -26,65 +26,68 @@ def get_recommendations():
         "https://i.imgur.com/Xj92Cjv.jpeg",
     ]
 
-def process(image: Image.Image):
+def gemma_image_analysis(image: Image.Image):
+    # Convert image to bytes and upload as a file
+    buf = io.BytesIO()
+    image.save(buf, format="PNG")
+    buf.seek(0)
+    img_bytes = buf.read()
+
+    # Upload image to Hugging Face hub and get a URL
+    img_url = client.upload_image(img_bytes)
+    # Compose multimodal message for Gemma-3
+    messages = [
+        {
+            "role": "system",
+            "content": [
+                {"type": "text", "text": "You are an expert ad analyst AI. Analyze the given ad and provide answers for three sections: 1. Category (one word) 2. Analysis (five sentences) 3. Five unique actionable improvement suggestions as a list starting with '- ' each. Output must be in three sections with clear headings."}
+            ]
+        },
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": img_url}},
+                {"type": "text", "text": "Please respond with:\n\nCategory:\n[category]\n\nAnalysis:\n[5 sentences]\n\nImprovement Suggestions:\n- [suggestion 1]\n- [suggestion 2]\n- [suggestion 3]\n- [suggestion 4]\n- [suggestion 5]\n\nEach suggestion must be unique and actionable."}
+            ]
+        }
+    ]
+
+    response = client.chat.completions.create(
+        model="google/gemma-3-4b-it",
+        messages=messages,
+        max_tokens=500
+    )
+
+    return response.choices[0].message["content"]
+
+def process(image):
     if image is None:
         return "", "", "", get_recommendations()
-    try:
-        # Convert PIL image to bytes
-        buf = io.BytesIO()
-        image.save(buf, format="PNG")
-        image_bytes = buf.getvalue()
-
-        prompt = (
-            "You are an expert ad analyst. "
-            "Given the uploaded image, return these sections:\n\n"
-            "Category: (a one or two word category, e.g. 'Fitness', 'Food', 'Travel')\n"
-            "Analysis: Write exactly five sentences about the ad's message, visuals, and emotional impact.\n"
-            "Suggestions: List five actionable and unique improvements for this ad. "
-            "Each suggestion must be one sentence and start with '- '. Suggestions must address different aspects: visuals, messaging, call-to-action, targeting, or layout."
-        )
-        messages = [
-            {
-                "role": "system",
-                "content": [{"type": "text", "text": "You are a helpful assistant."}]
-            },
-            {
-                "role": "user",
-                "content": [
-                    {"type": "image", "image": image_bytes},
-                    {"type": "text", "text": prompt}
-                ]
-            }
-        ]
-        # Model call
-        output = client.chat.completions.create(
-            model="google/gemma-3-4b-it",
-            messages=messages,
-            max_tokens=800
-        )
-        text = output.choices[0].message["content"]
 
-        # Simple parsing
-        cat, analysis, suggestions = "", "", ""
-        if "Category:" in text and "Analysis:" in text and "Suggestions:" in text:
-            cat = text.split("Category:")[1].split("Analysis:")[0].strip()
-            analysis = text.split("Analysis:")[1].split("Suggestions:")[0].strip()
-            suggestions = text.split("Suggestions:")[1].strip()
-        else:
-            # fallback: show all in analysis, nothing for cat/suggestions
-            analysis = text
+    full_output = gemma_image_analysis(image)
+    # Parse the response into 3 sections
+    cat, ana, sugs = "", "", ""
+    try:
+        parts = full_output.split("Analysis:")
+        if len(parts) >= 2:
+            cat = parts[0].replace("Category:", "").strip()
+            rest = parts[1].split("Improvement Suggestions:")
+            if len(rest) == 2:
+                ana = rest[0].strip()
+                sugs = rest[1].strip()
+            else:
+                ana = parts[1].strip()
+    except Exception:
+        cat, ana, sugs = "", "", full_output.strip()
 
-        return cat, analysis, suggestions, get_recommendations()
-    except Exception as e:
-        # For debugging
-        return "Error", "Error", "Error", ["Error"]
+    return cat, ana, sugs, get_recommendations()
 
 def main():
     with gr.Blocks(title="Smart Ad Analyzer (Gemma-3 Edition)") as demo:
         gr.Markdown("## 📢 Smart Ad Analyzer (Gemma-3 Edition)")
         gr.Markdown(
             """
-            Upload your ad image below and instantly get expert feedback.<br>
+            Upload your ad image below and instantly get expert feedback.  
             Category, analysis, improvement suggestions—and example ads for inspiration.
             """
         )
@@ -92,8 +95,8 @@ def main():
             inp = 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=7, interactive=False)
-                sug_out = gr.Textbox(label='🚀 Improvement Suggestions', lines=8, 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(