Add Agentic Refinement tab: iterative image→pattern→3D→projection→compare→refine loop

app.py

@@ -1,5 +1,5 @@
 """
-Garment Image -> 2D Sewing Pattern + Chat Editing + 3D Preview
+Garment Image -> 2D Sewing Pattern + Chat Editing + 3D Preview + Agentic Refinement
 """
 import json, os, re, traceback, copy
 from typing import Dict, Optional, Tuple, List
@@ -7,6 +7,7 @@ import gradio as gr
 from PIL import Image
 from pattern_generator import generate_pattern_from_analysis, get_pattern_pieces
 from garment_3d import create_3d_figure
+from refinement_loop import refinement_loop, render_3d_to_image
 
 GARMENT_ANALYSIS_PROMPT = """You are a professional fashion pattern maker. Analyze this garment image and extract precise sewing pattern parameters.
 
@@ -69,32 +70,23 @@ VISION_MODELS = [
     ("google/gemma-4-31B-it", "together", "Gemma 4 31B"),
     ("moonshotai/Kimi-K2.5", "together", "Kimi K2.5"),
 ]
-TEXT_MODELS = VISION_MODELS
 
 def _extract_response_text(message):
     content = message.get('content', '') or ''
     reasoning = message.get('reasoning', '') or ''
-    if content.strip():
-        return content.strip()
-    if reasoning.strip():
-        return reasoning.strip()
-    return ''
+    return content.strip() or reasoning.strip() or ''
 
 def _extract_json_from_text(text):
     json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', text)
-    if json_match:
-        return json_match.group(1)
+    if json_match: return json_match.group(1)
     json_match = re.search(r'\{[\s\S]*\}', text)
-    if json_match:
-        return json_match.group()
+    if json_match: return json_match.group()
     return None
 
 def _call_vlm(messages, timeout=180):
-    import requests, base64
-    from io import BytesIO
+    import requests
     hf_token = os.environ.get("HF_TOKEN", "")
-    if not hf_token:
-        return None
+    if not hf_token: return None
     for model_id, provider, display_name in VISION_MODELS:
         try:
             url = f"https://router.huggingface.co/{provider}/v1/chat/completions"
@@ -105,28 +97,23 @@ def _call_vlm(messages, timeout=180):
             if response.status_code == 200:
                 result = response.json()
                 text = _extract_response_text(result['choices'][0]['message'])
-                if not text:
-                    continue
+                if not text: continue
                 json_str = _extract_json_from_text(text)
-                if not json_str:
-                    continue
+                if not json_str: continue
                 analysis = json.loads(json_str)
-                analysis['_model_used'] = f"{display_name}"
+                analysis['_model_used'] = display_name
                 print(f"[VLM] OK: {display_name} detected {analysis.get('garment_type','?')}")
                 return analysis
-            else:
-                print(f"[VLM] {display_name}: HTTP {response.status_code}")
+            else: print(f"[VLM] {display_name}: HTTP {response.status_code}")
         except Exception as e:
-            print(f"[VLM] {display_name} failed: {e}")
-            continue
+            print(f"[VLM] {display_name} failed: {e}"); continue
     return None
 
 def analyze_with_vlm(image):
     import base64
     from io import BytesIO
     hf_token = os.environ.get("HF_TOKEN", "")
-    if not hf_token:
-        return None
+    if not hf_token: return None
     max_dim = 1024
     if max(image.size) > max_dim:
         ratio = max_dim / max(image.size)
@@ -155,21 +142,13 @@ def get_default_analysis(garment_type="shirt"):
 _current_analysis = {"data": None}
 
 def _generate_all_outputs(analysis):
-    """Generate 2D pattern, 3D figure (from pattern pieces), and summary."""
     garment_type = analysis.get('garment_type', 'shirt')
     measurements = analysis.get('measurements', {})
     features = analysis.get('features', {})
     params = {**measurements, **features}
-
-    # Generate 2D pattern pieces
     pattern_pieces = get_pattern_pieces(garment_type, params)
-
-    # Generate 2D pattern image and summary
     pattern_image, summary = generate_pattern_from_analysis(analysis)
-
-    # Generate 3D figure FROM the actual pattern pieces
     fig_3d = create_3d_figure(analysis, pattern_pieces=pattern_pieces)
-
     display = {k: v for k, v in analysis.items() if k != '_model_used'}
     model_info = f"\n\n*AI: {analysis.get('_model_used', 'Default')}*" if analysis.get('_model_used') else ""
     desc = analysis.get('description', 'No description')
@@ -181,10 +160,8 @@ def process_image(image, garment_type_override="Auto-detect"):
         return None, None, "Please upload a garment image or select a type.", "{}", []
     analysis = None
     if image is not None:
-        try:
-            analysis = analyze_with_vlm(image)
-        except Exception as e:
-            print(f"VLM failed: {e}")
+        try: analysis = analyze_with_vlm(image)
+        except Exception as e: print(f"VLM failed: {e}")
     if analysis is None:
         gt = garment_type_override.lower() if garment_type_override != "Auto-detect" else "shirt"
         analysis = get_default_analysis(gt)
@@ -201,33 +178,23 @@ def process_image(image, garment_type_override="Auto-detect"):
         return None, None, f"Error: {e}", "{}", []
 
 def process_text(description):
-    if not description.strip():
-        return None, None, "Enter a description.", "{}", []
+    if not description.strip(): return None, None, "Enter a description.", "{}", []
     analysis = None
     hf_token = os.environ.get("HF_TOKEN", "")
     if hf_token:
-        messages = [{"role": "user", "content": f"""Based on this garment description, extract sewing pattern parameters.
-
-Description: {description}
-
-Return ONLY JSON with: garment_type, description, measurements (bust, waist, hip, shoulder_width, bodice_length, sleeve_length, skirt_length, pant_length, neckline_depth, neckline_width, bicep, wrist, cap_height, collar_height, flare), features (has_collar, collar_type, has_cuffs, has_pockets, pocket_type, has_hood, fit)."""}]
+        messages = [{"role": "user", "content": f"Based on this garment description, extract sewing pattern parameters.\n\nDescription: {description}\n\nReturn ONLY JSON with: garment_type, description, measurements (bust, waist, hip, shoulder_width, bodice_length, sleeve_length, skirt_length, pant_length, neckline_depth, neckline_width, bicep, wrist, cap_height, collar_height, flare), features (has_collar, collar_type, has_cuffs, has_pockets, pocket_type, has_hood, fit)."}]
         analysis = _call_vlm(messages, timeout=90)
     if analysis is None:
         desc_lower = description.lower()
         for gt in ['hoodie','jacket','coat','blazer','dress','skirt','pants','trousers','jeans','vest','shirt','blouse','top']:
             if gt in desc_lower:
-                analysis = get_default_analysis(gt)
-                analysis['description'] = description
-                break
-        if analysis is None:
-            analysis = get_default_analysis("shirt")
-            analysis['description'] = description
+                analysis = get_default_analysis(gt); analysis['description'] = description; break
+        if analysis is None: analysis = get_default_analysis("shirt"); analysis['description'] = description
     _current_analysis["data"] = copy.deepcopy(analysis)
     try:
         p2d, p3d, summary, j = _generate_all_outputs(analysis)
         return p2d, p3d, summary, j, []
-    except Exception as e:
-        return None, None, f"Error: {e}", "{}", []
+    except Exception as e: return None, None, f"Error: {e}", "{}", []
 
 def process_manual(gt,bust,waist,hip,shoulder,bodice,sleeve,skirt,pant,neck,flare_c,collar,ctype,cuffs,pockets,hood,fit):
     analysis = {"garment_type":gt.lower(),"description":f"Custom {gt.lower()}","measurements":{"bust":bust,"waist":waist,"hip":hip,"shoulder_width":shoulder,"bodice_length":bodice,"sleeve_length":sleeve,"skirt_length":skirt,"pant_length":pant,"neckline_depth":neck,"neckline_width":7,"bicep":30,"wrist":18,"cap_height":14,"collar_height":5,"flare":flare_c},"features":{"has_collar":collar,"collar_type":ctype.lower(),"has_cuffs":cuffs,"has_pockets":pockets,"pocket_type":"patch","has_hood":hood,"fit":fit.lower()}}
@@ -235,79 +202,133 @@ def process_manual(gt,bust,waist,hip,shoulder,bodice,sleeve,skirt,pant,neck,flar
     try:
         p2d, p3d, summary, j = _generate_all_outputs(analysis)
         return p2d, p3d, summary, j, []
-    except Exception as e:
-        return None, None, f"Error: {e}", "{}", []
+    except Exception as e: return None, None, f"Error: {e}", "{}", []
 
 def chat_edit(message, history):
-    if not message.strip():
-        return history, None, None, "Please enter an edit request.", "{}"
-    current = _current_analysis.get("data")
-    if current is None:
-        current = get_default_analysis("shirt")
-        _current_analysis["data"] = current
+    if not message.strip(): return history, None, None, "Please enter an edit request.", "{}"
+    current = _current_analysis.get("data") or get_default_analysis("shirt")
+    _current_analysis["data"] = current
     current_clean = {k: v for k, v in current.items() if k != '_model_used'}
-    edit_prompt = EDIT_PROMPT_TEMPLATE.format(
-        current_json=json.dumps(current_clean, indent=2), user_message=message)
+    edit_prompt = EDIT_PROMPT_TEMPLATE.format(current_json=json.dumps(current_clean, indent=2), user_message=message)
     updated = None
-    hf_token = os.environ.get("HF_TOKEN", "")
-    if hf_token:
-        messages = [{"role": "user", "content": edit_prompt}]
-        try:
-            updated = _call_vlm(messages, timeout=90)
-        except Exception as e:
-            print(f"Edit VLM failed: {e}")
+    if os.environ.get("HF_TOKEN", ""):
+        try: updated = _call_vlm([{"role": "user", "content": edit_prompt}], timeout=90)
+        except: pass
     if updated is None:
         updated = copy.deepcopy(current)
         msg_lower = message.lower()
-        if "long sleeve" in msg_lower or "longer sleeve" in msg_lower:
-            updated['measurements']['sleeve_length'] = 65
-        elif "short sleeve" in msg_lower or "shorter sleeve" in msg_lower:
-            updated['measurements']['sleeve_length'] = 25
-        if "no collar" in msg_lower or "remove collar" in msg_lower:
-            updated['features']['has_collar'] = False; updated['features']['collar_type'] = 'none'
-        if "add collar" in msg_lower:
-            updated['features']['has_collar'] = True; updated['features']['collar_type'] = 'standard'
-        if "add hood" in msg_lower or "hoodie" in msg_lower:
-            updated['features']['has_hood'] = True
-        if "no hood" in msg_lower or "remove hood" in msg_lower:
-            updated['features']['has_hood'] = False
-        if "add pocket" in msg_lower:
-            updated['features']['has_pockets'] = True; updated['features']['pocket_type'] = 'patch'
-        if "no pocket" in msg_lower or "remove pocket" in msg_lower:
-            updated['features']['has_pockets'] = False
-        if "oversized" in msg_lower or "loose" in msg_lower:
-            updated['features']['fit'] = 'oversized'
-            updated['measurements']['bust'] = updated['measurements'].get('bust', 96) + 10
-        if "fitted" in msg_lower or "slim" in msg_lower:
-            updated['features']['fit'] = 'fitted'
-        if "flare" in msg_lower or "a-line" in msg_lower:
-            updated['measurements']['flare'] = max(updated['measurements'].get('flare', 0), 8)
-        if "straight" in msg_lower: updated['measurements']['flare'] = 0
-        if "mini" in msg_lower: updated['measurements']['skirt_length'] = 30
-        if "midi" in msg_lower: updated['measurements']['skirt_length'] = 80
-        if "maxi" in msg_lower: updated['measurements']['skirt_length'] = 110
+        if "long sleeve" in msg_lower: updated['measurements']['sleeve_length'] = 65
+        elif "short sleeve" in msg_lower: updated['measurements']['sleeve_length'] = 25
+        if "no collar" in msg_lower: updated['features']['has_collar'] = False; updated['features']['collar_type'] = 'none'
+        if "add collar" in msg_lower: updated['features']['has_collar'] = True; updated['features']['collar_type'] = 'standard'
+        if "add hood" in msg_lower: updated['features']['has_hood'] = True
+        if "no hood" in msg_lower: updated['features']['has_hood'] = False
+        if "oversized" in msg_lower: updated['features']['fit'] = 'oversized'; updated['measurements']['bust'] = updated['measurements'].get('bust', 96) + 10
+        if "fitted" in msg_lower: updated['features']['fit'] = 'fitted'
+        if "flare" in msg_lower: updated['measurements']['flare'] = max(updated['measurements'].get('flare', 0), 8)
         updated['_model_used'] = 'Rule-based edit'
-    if 'garment_type' not in updated:
-        updated['garment_type'] = current.get('garment_type', 'shirt')
+    if 'garment_type' not in updated: updated['garment_type'] = current.get('garment_type', 'shirt')
     _current_analysis["data"] = copy.deepcopy(updated)
-    try:
-        p2d, p3d, summary, j = _generate_all_outputs(updated)
-    except Exception as e:
-        p2d, p3d, summary, j = None, None, f"Error: {e}", "{}"
-    ai_msg = f"Applied edit: {message}\n\n"
+    try: p2d, p3d, summary, j = _generate_all_outputs(updated)
+    except Exception as e: p2d, p3d, summary, j = None, None, f"Error: {e}", "{}"
+    ai_msg = f"Applied: {message}\n"
     changes = []
     for k in set(list(current.get('measurements',{}).keys()) + list(updated.get('measurements',{}).keys())):
         ov, nv = current.get('measurements',{}).get(k), updated.get('measurements',{}).get(k)
-        if ov != nv and ov is not None and nv is not None:
-            changes.append(f"  {k}: {ov} -> {nv}")
+        if ov != nv and ov is not None and nv is not None: changes.append(f"  {k}: {ov} → {nv}")
     for k in set(list(current.get('features',{}).keys()) + list(updated.get('features',{}).keys())):
         ov, nv = current.get('features',{}).get(k), updated.get('features',{}).get(k)
-        if ov != nv: changes.append(f"  {k}: {ov} -> {nv}")
-    ai_msg += ("Changes:\n" + "\n".join(changes)) if changes else "No changes detected."
-    history = history or []
-    history.append((message, ai_msg))
+        if ov != nv: changes.append(f"  {k}: {ov} → {nv}")
+    ai_msg += ("\n".join(changes)) if changes else "No changes."
+    history = history or []; history.append((message, ai_msg))
     return history, p2d, p3d, summary, j
 
+# ── Agentic Refinement ──────────────────────────────────────────────────────
+def run_refinement(image, garment_type_override, max_iters):
+    """Run the agentic refinement loop."""
+    if image is None:
+        yield None, None, None, "Please upload a garment image.", "{}", None
+        return
+
+    # Step 1: Initial VLM analysis
+    analysis = None
+    try:
+        analysis = analyze_with_vlm(image)
+    except Exception as e:
+        print(f"VLM failed: {e}")
+
+    if analysis is None:
+        gt = garment_type_override.lower() if garment_type_override != "Auto-detect" else "shirt"
+        analysis = get_default_analysis(gt)
+
+    if garment_type_override != "Auto-detect":
+        analysis['garment_type'] = garment_type_override.lower()
+
+    # Generate function for the loop
+    def gen_fn(a):
+        return _generate_all_outputs(a)
+
+    # Run refinement loop
+    max_iters = int(max_iters)
+    result = refinement_loop(
+        original_image=image,
+        initial_analysis=analysis,
+        generate_fn=gen_fn,
+        max_iterations=max_iters,
+        target_composite=0.82,
+        plateau_patience=3,
+        lr=0.7,
+    )
+
+    # Build log markdown
+    log_lines = [f"## Refinement Results\n"]
+    log_lines.append(f"**Converged:** {'✅ Yes' if result['converged'] else '❌ No'}")
+    log_lines.append(f"**Iterations:** {result['total_iterations']}")
+    log_lines.append(f"**Best Score:** {result['best_score']:.4f}")
+    if result['scores']:
+        log_lines.append(f"**Score progression:** {' → '.join(f'{s:.3f}' for s in result['scores'])}")
+    log_lines.append("")
+
+    for step in result['history']:
+        it = step['iteration']
+        status = step.get('status', '?')
+        metrics = step.get('metrics', {})
+        log_lines.append(f"### Iteration {it} — {status}")
+        if metrics:
+            log_lines.append(f"SSIM={metrics.get('ssim',0):.3f} | Edge={metrics.get('edge_ssim',0):.3f} | Composite={metrics.get('composite',0):.3f}")
+        if step.get('new_best'):
+            log_lines.append("⭐ **New best!**")
+        diffs = step.get('vlm_differences', [])
+        if diffs:
+            log_lines.append("**Differences:** " + "; ".join(diffs[:3]))
+        adj = step.get('adjustments', {})
+        if adj:
+            log_lines.append("**Adjustments:** " + ", ".join(f"{k}={v}" for k, v in adj.items()))
+        reason = step.get('reason', '')
+        if reason:
+            log_lines.append(f"*{reason}*")
+        log_lines.append("")
+
+    log_md = "\n".join(log_lines)
+
+    # Get best outputs
+    best = result['best_analysis']
+    _current_analysis["data"] = copy.deepcopy(best)
+    try:
+        p2d, p3d, summary, j = _generate_all_outputs(best)
+    except:
+        p2d, p3d, summary, j = None, None, "Error generating final outputs", "{}"
+
+    # Get last projection
+    last_proj = None
+    for step in reversed(result['history']):
+        if 'projection' in step:
+            last_proj = step['projection']
+            break
+
+    yield p2d, p3d, last_proj, log_md, j, summary
+
+# ── UI ──────────────────────────────────────────────────────────────────────
 CSS = """
 .main-header { text-align: center; margin-bottom: 20px; }
 .info-box { padding: 15px; border-radius: 10px; background: #f0f7ff; border: 1px solid #cce0ff; margin: 10px 0; }
@@ -315,18 +336,17 @@ CSS = """
 """
 
 with gr.Blocks(title="Garment Pattern Studio") as demo:
-    gr.HTML("""
-    <div class="main-header">
+    gr.HTML("""<div class="main-header">
         <h1>🧵 Garment Pattern Studio</h1>
-        <p style="font-size: 1.1em; color: #555;">Analyze garments, edit with chat, preview in 3D</p>
+        <p style="font-size: 1.1em; color: #555;">Analyze garments, edit with chat, preview in 3D, refine with AI agent</p>
     </div>
     <div class="info-box">
-        <b>Powered by:</b> Qwen 3.5 &middot; Gemma 4 &middot; Kimi K2.5 via
+        <b>Powered by:</b> Qwen 3.5 · Gemma 4 · Kimi K2.5 via
         <a href="https://huggingface.co/docs/inference-providers">HF Inference Providers</a>
-        &nbsp;|&nbsp; <b>3D view is built from the actual 2D pattern pieces</b>
+        &nbsp;|&nbsp; <b>3D view built from actual 2D pattern pieces</b>
     </div>""")
 
-    with gr.Tab("From Image"):
+    with gr.Tab("📸 From Image"):
         with gr.Row():
             with gr.Column(scale=1):
                 input_image = gr.Image(type="pil", label="Upload Garment Image", height=350)
@@ -337,10 +357,10 @@ with gr.Blocks(title="Garment Pattern Studio") as demo:
                     with gr.Column(): out_pattern_2d = gr.Image(label="2D Sewing Pattern", height=400)
                     with gr.Column(): out_3d = gr.Plot(label="3D Garment Preview")
                 out_summary = gr.Markdown(label="Pattern Summary")
-                with gr.Accordion("Raw JSON", open=False): out_json = gr.Code(language="json", label="Analysis")
+                with gr.Accordion("Raw JSON", open=False): out_json = gr.Code(language="json")
         analyze_btn.click(process_image, inputs=[input_image, garment_override], outputs=[out_pattern_2d, out_3d, out_summary, out_json])
 
-    with gr.Tab("From Text"):
+    with gr.Tab("✍️ From Text"):
         with gr.Row():
             with gr.Column(scale=1):
                 text_input = gr.Textbox(label="Describe the garment", placeholder="e.g., A fitted A-line dress with cap sleeves", lines=3)
@@ -361,36 +381,20 @@ with gr.Blocks(title="Garment Pattern Studio") as demo:
                 with gr.Accordion("Raw JSON", open=False): txt_json = gr.Code(language="json")
         text_btn.click(process_text, inputs=[text_input], outputs=[txt_pattern_2d, txt_3d, txt_summary, txt_json])
 
-    with gr.Tab("Manual Parameters"):
+    with gr.Tab("📐 Manual"):
         with gr.Row():
             with gr.Column(scale=1):
                 m_type = gr.Dropdown(choices=["Shirt","Dress","Skirt","Pants","Jacket","Hoodie","Vest"], value="Shirt", label="Garment Type")
                 gr.Markdown("### Measurements (cm)")
-                with gr.Row():
-                    m_bust = gr.Slider(70,130,value=92,step=1,label="Bust")
-                    m_waist = gr.Slider(55,110,value=74,step=1,label="Waist")
-                with gr.Row():
-                    m_hip = gr.Slider(75,130,value=96,step=1,label="Hip")
-                    m_shoulder = gr.Slider(35,55,value=42,step=1,label="Shoulder")
-                with gr.Row():
-                    m_bodice = gr.Slider(30,80,value=42,step=1,label="Bodice Length")
-                    m_sleeve = gr.Slider(10,75,value=60,step=1,label="Sleeve Length")
-                with gr.Row():
-                    m_skirt = gr.Slider(25,120,value=55,step=1,label="Skirt Length")
-                    m_pant = gr.Slider(25,115,value=100,step=1,label="Pant Length")
-                with gr.Row():
-                    m_neck = gr.Slider(3,25,value=8,step=1,label="Neckline Depth")
-                    m_flare = gr.Slider(0,20,value=0,step=1,label="Hem Flare")
+                with gr.Row(): m_bust = gr.Slider(70,130,value=92,step=1,label="Bust"); m_waist = gr.Slider(55,110,value=74,step=1,label="Waist")
+                with gr.Row(): m_hip = gr.Slider(75,130,value=96,step=1,label="Hip"); m_shoulder = gr.Slider(35,55,value=42,step=1,label="Shoulder")
+                with gr.Row(): m_bodice = gr.Slider(30,80,value=42,step=1,label="Bodice Length"); m_sleeve = gr.Slider(10,75,value=60,step=1,label="Sleeve Length")
+                with gr.Row(): m_skirt = gr.Slider(25,120,value=55,step=1,label="Skirt Length"); m_pant = gr.Slider(25,115,value=100,step=1,label="Pant Length")
+                with gr.Row(): m_neck = gr.Slider(3,25,value=8,step=1,label="Neckline Depth"); m_flare = gr.Slider(0,20,value=0,step=1,label="Hem Flare")
                 gr.Markdown("### Features")
-                with gr.Row():
-                    m_collar = gr.Checkbox(value=True,label="Collar")
-                    m_ctype = gr.Dropdown(["Standard","Mandarin","Peter_pan"],value="Standard",label="Collar Type")
-                with gr.Row():
-                    m_cuffs = gr.Checkbox(value=True,label="Cuffs")
-                    m_pockets = gr.Checkbox(value=False,label="Pockets")
-                with gr.Row():
-                    m_hood = gr.Checkbox(value=False,label="Hood")
-                    m_fit = gr.Dropdown(["Fitted","Regular","Oversized","Loose"],value="Regular",label="Fit")
+                with gr.Row(): m_collar = gr.Checkbox(value=True,label="Collar"); m_ctype = gr.Dropdown(["Standard","Mandarin","Peter_pan"],value="Standard",label="Collar Type")
+                with gr.Row(): m_cuffs = gr.Checkbox(value=True,label="Cuffs"); m_pockets = gr.Checkbox(value=False,label="Pockets")
+                with gr.Row(): m_hood = gr.Checkbox(value=False,label="Hood"); m_fit = gr.Dropdown(["Fitted","Regular","Oversized","Loose"],value="Regular",label="Fit")
                 manual_btn = gr.Button("Generate Pattern", variant="primary", size="lg")
             with gr.Column(scale=2):
                 with gr.Row():
@@ -400,31 +404,60 @@ with gr.Blocks(title="Garment Pattern Studio") as demo:
                 with gr.Accordion("Raw JSON", open=False): man_json = gr.Code(language="json")
         manual_btn.click(process_manual, inputs=[m_type,m_bust,m_waist,m_hip,m_shoulder,m_bodice,m_sleeve,m_skirt,m_pant,m_neck,m_flare,m_collar,m_ctype,m_cuffs,m_pockets,m_hood,m_fit], outputs=[man_pattern_2d, man_3d, man_summary, man_json])
 
-    with gr.Tab("Chat & Edit"):
-        gr.Markdown("### Edit your pattern with natural language\nFirst generate a pattern, then edit here. Both 2D and 3D update together.")
+    with gr.Tab("💬 Chat & Edit"):
+        gr.Markdown("### Edit pattern with natural language\nGenerate a pattern first, then refine here.")
         with gr.Row():
             with gr.Column(scale=1):
                 chatbot = gr.Chatbot(label="Pattern Editor", height=400)
-                chat_input = gr.Textbox(label="Edit instruction", placeholder="e.g., Make the sleeves longer, Add a hood, Change to A-line skirt", lines=2)
-                with gr.Row():
-                    chat_send = gr.Button("Apply Edit", variant="primary")
-                    chat_clear = gr.Button("Clear Chat")
+                chat_input = gr.Textbox(label="Edit instruction", placeholder="e.g., Make sleeves longer, Add a hood", lines=2)
+                with gr.Row(): chat_send = gr.Button("Apply Edit", variant="primary"); chat_clear = gr.Button("Clear")
             with gr.Column(scale=2):
                 with gr.Row():
-                    with gr.Column(): chat_pattern_2d = gr.Image(label="Updated 2D Pattern", height=400)
-                    with gr.Column(): chat_3d = gr.Plot(label="Updated 3D Preview")
+                    with gr.Column(): chat_pattern_2d = gr.Image(label="Updated 2D", height=400)
+                    with gr.Column(): chat_3d = gr.Plot(label="Updated 3D")
                 chat_summary = gr.Markdown()
-                with gr.Accordion("Updated JSON", open=False): chat_json = gr.Code(language="json")
+                with gr.Accordion("JSON", open=False): chat_json = gr.Code(language="json")
         def clear_chat(): return [], None, None, "", "{}"
         chat_send.click(chat_edit, inputs=[chat_input, chatbot], outputs=[chatbot, chat_pattern_2d, chat_3d, chat_summary, chat_json])
         chat_input.submit(chat_edit, inputs=[chat_input, chatbot], outputs=[chatbot, chat_pattern_2d, chat_3d, chat_summary, chat_json])
         chat_clear.click(clear_chat, outputs=[chatbot, chat_pattern_2d, chat_3d, chat_summary, chat_json])
 
+    with gr.Tab("🔄 Agentic Refinement"):
+        gr.Markdown("""### Iterative Refinement Loop
+Upload a garment image. The AI agent will:
+1. **Analyze** → extract initial pattern parameters
+2. **Generate** → create 2D pattern + 3D garment
+3. **Project** → render 3D to 2D front view
+4. **Compare** → measure similarity (SSIM + VLM visual comparison)
+5. **Refine** → VLM suggests parameter adjustments
+6. **Repeat** until convergence or max iterations
+
+*Requires HF_TOKEN for VLM-powered refinement.*""")
+        with gr.Row():
+            with gr.Column(scale=1):
+                refine_image = gr.Image(type="pil", label="Upload Garment Image", height=300)
+                refine_type = gr.Dropdown(choices=["Auto-detect","Shirt","Dress","Skirt","Pants","Jacket","Hoodie","Vest"], value="Auto-detect", label="Garment Type")
+                refine_iters = gr.Slider(1, 15, value=5, step=1, label="Max Iterations")
+                refine_btn = gr.Button("🚀 Start Refinement", variant="primary", size="lg")
+            with gr.Column(scale=2):
+                with gr.Row():
+                    with gr.Column(): refine_2d = gr.Image(label="Best 2D Pattern", height=350)
+                    with gr.Column(): refine_proj = gr.Image(label="3D→2D Projection", height=350)
+                with gr.Row():
+                    with gr.Column(): refine_3d = gr.Plot(label="Best 3D Preview")
+                    with gr.Column(): refine_log = gr.Markdown(label="Refinement Log")
+                refine_summary = gr.Markdown()
+                with gr.Accordion("Best Parameters JSON", open=False): refine_json = gr.Code(language="json")
+
+        refine_btn.click(run_refinement,
+                         inputs=[refine_image, refine_type, refine_iters],
+                         outputs=[refine_2d, refine_3d, refine_proj, refine_log, refine_json, refine_summary])
+
     gr.HTML("""<div class="ref-box" style="margin-top: 20px;"><h4>Research References</h4><ul>
-        <li><b>ChatGarment</b> (2024) [<a href="https://arxiv.org/abs/2412.17811">Paper</a>] [<a href="https://huggingface.co/datasets/sy000/ChatGarmentDataset">Dataset</a>]</li>
-        <li><b>NGL-Prompter</b> (2025) [<a href="https://arxiv.org/abs/2602.20700">Paper</a>]</li>
-        <li><b>SewFormer</b> (2023) [<a href="https://arxiv.org/abs/2311.04218">Paper</a>]</li>
-        <li><b>GarmentDiffusion</b> (2025) [<a href="https://arxiv.org/abs/2504.21476">Paper</a>]</li>
+        <li><b>ChatGarment</b> (2024) — VLM + dialogue for garment editing [<a href="https://arxiv.org/abs/2412.17811">Paper</a>]</li>
+        <li><b>NGL-Prompter</b> (2025) — Training-free VLM pattern estimation [<a href="https://arxiv.org/abs/2602.20700">Paper</a>]</li>
+        <li><b>RRVF</b> (2025) — Render-compare visual feedback loops [<a href="https://arxiv.org/abs/2507.20766">Paper</a>]</li>
+        <li><b>SceneAssistant</b> (2026) — Agentic VLM scene refinement [<a href="https://arxiv.org/abs/2603.12238">Paper</a>]</li>
     </ul></div>""")
 
 if __name__ == "__main__":