Update MapTrace Gradio demo

README.md

@@ -1,12 +1,18 @@
 ---
 title: MapTrace Path Planner
-emoji: 🐨
-colorFrom: red
-colorTo: pink
+emoji: 🗺️
+colorFrom: green
+colorTo: blue
 sdk: gradio
-sdk_version: 6.8.0
+sdk_version: "5.0.0"
 app_file: app.py
-pinned: false
+pinned: true
+license: apache-2.0
+short_description: Visual path planning on maps with Qwen3.5-0.8B
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# MapTrace Path Planner
+
+Qwen3.5-0.8B with Partial Fine-Tuning on google/MapTrace dataset.
+
+Upload a map image, enter start (green) and end (red) coordinates in normalized [0,1] space, and see the predicted traversable path overlaid on the image.

app.py

@@ -0,0 +1,245 @@
+#!/usr/bin/env python3
+"""
+Qwen3.5-0.8B MapTrace - Gradio Demo
+Predicts a traversable path between start (green) and end (red) locations on a map image.
+"""
+
+import re
+import ast
+import math
+import torch
+import gradio as gr
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+# ── CONFIG ────────────────────────────────────────────────────────────────────
+MODEL_ID = "TurkishCodeMan/Qwen3.5-0.8B-MapTrace-PartialFT"  # HF repo adın
+
+# ── MODEL YÜKLEME (Uygulama başlatılınca bir kez) ─────────────────────────────
+print(f"Loading model: {MODEL_ID}")
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model.eval()
+print("✅ Model loaded and ready!")
+
+
+# ── YARDIMCI FONKSİYONLAR ────────────────────────────────────────────────────
+
+def parse_coordinates(text: str) -> list[tuple[float, float]]:
+    """Model çıktısından koordinatları ayıklar."""
+    # Pattern 1: [(x, y), ...] formatı
+    coords = re.findall(r"\((-?\d+\.?\d*),\s*(-?\d+\.?\d*)\)", text)
+    if coords:
+        return [(float(x), float(y)) for x, y in coords]
+    return []
+
+
+def draw_path_on_image(
+    image: Image.Image,
+    path_coords: list[tuple[float, float]],
+    start_xy: tuple[float, float],
+    end_xy: tuple[float, float],
+) -> Image.Image:
+    """
+    Harita görselinin üzerine tahmin edilen yolu çizer.
+    - Yol: Kalın sarı çizgi + turuncu noktalar
+    - Start: Parlak yeşil daire
+    - End  : Parlak kırmızı daire
+    """
+    img = image.copy().convert("RGBA")
+    overlay = Image.new("RGBA", img.size, (0, 0, 0, 0))
+    draw = ImageDraw.Draw(overlay)
+
+    W, H = img.size
+
+    def norm_to_px(x, y):
+        return int(x * W), int(y * H)
+
+    # Yol Çizimi
+    if len(path_coords) >= 2:
+        pixel_path = [norm_to_px(*p) for p in path_coords]
+
+        # Yol gölgesi (siyah, kalın)
+        draw.line(pixel_path, fill=(0, 0, 0, 160), width=7)
+        # Ana yol (sarı)
+        draw.line(pixel_path, fill=(255, 215, 0, 230), width=4)
+
+        # Ara noktalar
+        for px, py in pixel_path[1:-1]:
+            r = 5
+            draw.ellipse([(px - r, py - r), (px + r, py + r)], fill=(255, 140, 0, 255))
+
+    # Başlangıç noktası (yeşil)
+    sx, sy = norm_to_px(*start_xy)
+    draw.ellipse([(sx - 10, sy - 10), (sx + 10, sy + 10)], fill=(0, 200, 80, 255), outline=(255, 255, 255, 255), width=2)
+
+    # Bitiş noktası (kırmızı)
+    ex, ey = norm_to_px(*end_xy)
+    draw.ellipse([(ex - 10, ey - 10), (ex + 10, ey + 10)], fill=(220, 40, 40, 255), outline=(255, 255, 255, 255), width=2)
+
+    # Composite
+    result = Image.alpha_composite(img, overlay).convert("RGB")
+    return result
+
+
+def run_inference(image: Image.Image, start_x: float, start_y: float, end_x: float, end_y: float):
+    """
+    Model çalıştırır, koordinatları parse eder, görsele yolu çizer.
+    """
+    if image is None:
+        return None, "❌ Lütfen bir harita görseli yükleyin."
+
+    # Prompt oluştur
+    prompt_text = (
+        f"You are provided an image of a path with a start location denoted in green "
+        f"and an end location denoted in red. \n"
+        f"The normalized xy-coordinates of the start location are ({start_x}, {start_y}) "
+        f"and of the end location ({end_x}, {end_y}). \n"
+        f"Output a list of normalized coordinates in the form of a list [(x1,y1), (x2,y2)...] "
+        f"of the path between the start and end location. \n"
+        f"Ensure that the path follows the traversable locations of the map."
+    )
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": prompt_text},
+            ],
+        }
+    ]
+
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    inputs = processor(
+        text=[text],
+        images=[image],
+        return_tensors="pt",
+        padding=True,
+        min_pixels=256 * 28 * 28,
+        max_pixels=1024 * 768,
+    ).to(model.device)
+
+    with torch.no_grad():
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            do_sample=False,
+            temperature=0.0,
+        )
+
+    raw_output = processor.decode(
+        generated_ids[0][inputs.input_ids.shape[1]:],
+        skip_special_tokens=True
+    ).strip()
+
+    # Koordinatları parse et
+    path = parse_coordinates(raw_output)
+
+    if not path:
+        return image, f"⚠️ Model geçerli koordinat üretemedi.\n\nHam çıktı:\n{raw_output}"
+
+    # Yolu görsele çiz
+    result_img = draw_path_on_image(image, path, (start_x, start_y), (end_x, end_y))
+
+    # Sonuç metni
+    path_str = " → ".join([f"({x:.4f}, {y:.4f})" for x, y in path])
+    info_text = (
+        f"✅ Tahmin edilen yol: **{len(path)} nokta**\n\n"
+        f"`{path_str}`\n\n"
+        f"---\n*Ham model çıktısı:*\n```\n{raw_output[:500]}\n```"
+    )
+
+    return result_img, info_text
+
+
+# ── GRADIO ARAYÜZÜ ────────────────────────────────────────────────────────────
+
+DESCRIPTION = """
+# 🗺️ MapTrace Path Planner
+**Model:** [Qwen3.5-0.8B-MapTrace-PartialFT](https://huggingface.co/TurkishCodeMan/Qwen3.5-0.8B-MapTrace-PartialFT)
+
+Upload a map image and enter normalized start/end coordinates. The model will predict a traversable path between the two locations and overlay it on the map.
+
+**Coordinates:** Normalized (x, y) values in [0, 1] range. Top-left = (0, 0), Bottom-right = (1, 1).
+"""
+
+EXAMPLES = [
+    ["example_map.png", 0.77, 0.47, 0.54, 0.54],
+]
+
+with gr.Blocks(
+    title="MapTrace Path Planner",
+    theme=gr.themes.Soft(primary_hue="emerald", secondary_hue="slate"),
+    css="""
+        .gradio-container { max-width: 1100px !important; }
+        .result-image img { border-radius: 12px; box-shadow: 0 4px 20px rgba(0,0,0,0.15); }
+        footer { display: none !important; }
+    """,
+) as demo:
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Row():
+        # Sol panel: Giriş
+        with gr.Column(scale=1):
+            gr.Markdown("### 📤 Input")
+            input_image = gr.Image(
+                label="Map Image",
+                type="pil",
+                sources=["upload"],
+                height=350,
+            )
+            with gr.Row():
+                start_x = gr.Number(label="Start X", value=0.77, minimum=0.0, maximum=1.0, step=0.01, precision=4)
+                start_y = gr.Number(label="Start Y", value=0.47, minimum=0.0, maximum=1.0, step=0.01, precision=4)
+            with gr.Row():
+                end_x = gr.Number(label="End X", value=0.54, minimum=0.0, maximum=1.0, step=0.01, precision=4)
+                end_y = gr.Number(label="End Y", value=0.54, minimum=0.0, maximum=1.0, step=0.01, precision=4)
+
+            run_btn = gr.Button("🚀 Predict Path", variant="primary", size="lg")
+
+        # Right panel: Output
+        with gr.Column(scale=1):
+            gr.Markdown("### 📍 Result")
+            output_image = gr.Image(
+                label="Predicted Path",
+                type="pil",
+                interactive=False,
+                height=350,
+                elem_classes=["result-image"],
+            )
+            output_info = gr.Markdown(label="Info", value="*Results will appear here after running the model.*")
+
+    # Etiketler (Görsel referans)
+    with gr.Row():
+        gr.Markdown("""
+        ---
+        🟢 **Start**   |   🔴 **End**   |   🟡 **Predicted Path**
+        """)
+
+    # Event
+    run_btn.click(
+        fn=run_inference,
+        inputs=[input_image, start_x, start_y, end_x, end_y],
+        outputs=[output_image, output_info],
+        api_name="predict_path",
+        show_progress="full",
+    )
+
+    # Enter ile de çalıştırılabilsin
+    for component in [start_x, start_y, end_x, end_y]:
+        component.submit(
+            fn=run_inference,
+            inputs=[input_image, start_x, start_y, end_x, end_y],
+            outputs=[output_image, output_info],
+        )
+
+if __name__ == "__main__":
+    demo.launch(share=False)

requirements.txt

@@ -0,0 +1,7 @@
+transformers>=4.51.0
+torch>=2.1.0
+gradio>=5.0.0
+Pillow>=10.0.0
+accelerate>=0.26.0
+huggingface_hub>=0.21.0
+numpy>=1.24.0

upload_space.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""
+HuggingFace Space Upload Script
+Bu script gradio_demo klasörünü HF Spaces'e yükler.
+"""
+
+from huggingface_hub import HfApi, create_repo
+
+# ── CONFIG ────────────────────────────────────────────────────────────────────
+# 👇 Kendi HF kullanıcı adın ve space adını buraya yaz
+HF_USERNAME  = "TurkishCodeMan"
+SPACE_NAME   = "MapTrace-Path-Planner"
+SPACE_DIR    = "./"   # Bu script gradio_demo klasöründen çalıştırılır
+
+REPO_ID = f"{HF_USERNAME}/{SPACE_NAME}"
+
+
+def main():
+    api = HfApi()
+
+    print("=" * 55)
+    print("🚀 HuggingFace Space Upload")
+    print("=" * 55)
+    print(f"  Repo  : {REPO_ID}")
+    print(f"  SDK   : gradio")
+    print("=" * 55)
+
+    # 1. Space oluştur (zaten varsa atla)
+    print("\n📦 Space oluşturuluyor / kontrol ediliyor...")
+    create_repo(
+        repo_id=REPO_ID,
+        repo_type="space",
+        space_sdk="gradio",
+        exist_ok=True,
+        private=False,
+    )
+    print(f"  ✓ Space hazır: https://huggingface.co/spaces/{REPO_ID}")
+
+    # 2. Klasörü yükle
+    print(f"\n📤 Dosyalar yükleniyor: {SPACE_DIR} → {REPO_ID}")
+    api.upload_folder(
+        folder_path=SPACE_DIR,
+        repo_id=REPO_ID,
+        repo_type="space",
+        commit_message="Update MapTrace Gradio demo",
+        # upload_space.py'nin kendisini IGNORE listesine ekle (gereksiz)
+        ignore_patterns=["*.pyc", "__pycache__", ".git"],
+    )
+
+    print("\n✅ YÜKLEME TAMAMLANDI!")
+    print(f"   Space URL : https://huggingface.co/spaces/{REPO_ID}")
+    print(f"   Birkaç dakika içinde Space build edilecektir.")
+
+
+if __name__ == "__main__":
+    main()