Update app.py

app.py

@@ -1,83 +1,105 @@
-import gradio as gr
-import cv2
+import os
 import time
-from ultralytics import YOLO
+import cv2
 import numpy as np
-
-# Model yükle
-model = YOLO("best.pt")
-device = 0  # GPU cihazı (0 = ilk GPU)
-
-def infer_fire(image, conf_thresh):
-    # RGB→BGR çevir, infer
-    img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    start = time.time()
-    res = model.predict(
-        source=img_bgr,
-        device=device,
-        imgsz=640,
-        conf=conf_thresh
-    )[0]
-    elapsed = (time.time() - start) * 1000  # ms cinsinden
-
-    annotated = img_bgr.copy()
-    h, w = annotated.shape[:2]
-    boxes_np = (
-        res.boxes.data.cpu().numpy()
-        if hasattr(res.boxes, "data")
-        else np.empty((0, 6))
+import gradio as gr
+import torch
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+# ——————————————
+# 1) MODEL AĞIRLIKLARINI İNDİR / YÜKLE
+# ——————————————
+MODEL_FILENAME = "best.pt"
+if not os.path.isfile(MODEL_FILENAME):
+    MODEL_PATH = hf_hub_download(
+        repo_id="APIMONSTER/ADA447",   # kendi Space adınızı yazın
+        filename=MODEL_FILENAME,
+        repo_type="space"
     )
+else:
+    MODEL_PATH = MODEL_FILENAME
 
-    for x1, y1, x2, y2, conf, cls in boxes_np:
-        x1, y1, x2, y2 = map(int, (x1, y1, x2, y2))
-        # 1) Box'u resim sınırları içinde tut
-        x1 = max(0, x1);   y1 = max(0, y1)
-        x2 = min(w-1, x2); y2 = min(h-1, y2)
+# ——————————————
+# 2) CİHAZI BELİRLE
+# ——————————————
+device = 0 if torch.cuda.is_available() else "cpu"
 
-        label = f"{model.names[int(cls)]} {conf:.2f}"
+# ——————————————
+# 3) MODELİ YÜKLE
+# ——————————————
+model = YOLO(MODEL_PATH)  # ultralytics kendi içinde cihazı algılar
+# ama predict içinde yine device argümanı geçiyoruz
 
-        # 2) Kalın box çiz
-        cv2.rectangle(annotated, (x1, y1), (x2, y2), (0, 255, 0), 4)
-
-        # 3) Yazı boyutunu al
-        (text_w, text_h), baseline = cv2.getTextSize(
-            label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 3
-        )
-
-        # 4) Etiket pozisyonunu ayarla
-        text_x = x1
-        text_y = y1 - 5
-        # Üst sınırı aşmasın
-        if text_y - text_h - baseline < 0:
-            text_y = y1 + text_h + 5
-        # Sağ sınırı aşmasın
-        if text_x + text_w > w:
-            text_x = w - text_w - 5
-
-        # 5) Dolgu arkaplanlı dikdörtgen
-        cv2.rectangle(
-            annotated,
-            (text_x, text_y - text_h - baseline),
-            (text_x + text_w, text_y + baseline),
-            (0, 255, 0),
-            cv2.FILLED
+def infer_fire(image, conf_thresh):
+    if image is None:
+        return None, "⚠️ Please upload an image."
+
+    try:
+        # 1) Renk uzayını dönüştür
+        img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        start = time.time()
+
+        # 2) Tahmin
+        results = model.predict(
+            source=img_bgr,
+            device=device,
+            imgsz=640,
+            conf=conf_thresh
         )
+        res = results[0]
+        elapsed = (time.time() - start) * 1000
+
+        # 3) Annotate
+        annotated = img_bgr.copy()
+        h, w = annotated.shape[:2]
+        boxes = res.boxes.data.cpu().numpy() if hasattr(res.boxes, "data") else np.empty((0,6))
+
+        for x1, y1, x2, y2, conf, cls in boxes:
+            x1, y1, x2, y2 = map(int, (x1, y1, x2, y2))
+            # sınır dışına taşan coord’ları clamp et
+            x1, y1 = max(0, x1), max(0, y1)
+            x2, y2 = min(w-1, x2), min(h-1, y2)
+
+            label = f"{model.names[int(cls)]} {conf:.2f}"
+            # kalın kutu
+            cv2.rectangle(annotated, (x1, y1), (x2, y2), (0,255,0), 4)
+
+            # yazı boyutu, arka plan ve yerleşim
+            (tw, th), bl = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 3)
+            tx, ty = x1, y1 - 5
+            if ty - th - bl < 0:
+                ty = y1 + th + 5
+            if tx + tw > w:
+                tx = w - tw - 5
+
+            cv2.rectangle(
+                annotated,
+                (tx, ty-th-bl),
+                (tx+tw, ty+bl),
+                (0,255,0),
+                cv2.FILLED
+            )
+            cv2.putText(
+                annotated,
+                label,
+                (tx, ty),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.7,
+                (0,0,0),
+                3
+            )
 
-        # 6) Siyah renkli yazı
-        cv2.putText(
-            annotated,
-            label,
-            (text_x, text_y),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.7,
-            (0, 0, 0),
-            3
-        )
+        out_img = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
+        return out_img, f"✅ Inference: {elapsed:.1f} ms"
 
-    out_img = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
-    return out_img, f"Inference time: {elapsed:.1f} ms"
+    except Exception as e:
+        # hata mesajını kullanıcıya göster
+        return image, f"❌ Error: {str(e)}"
 
-# Gradio arayüzü
+# ——————————————
+# 4) GRADIO ARAYÜZÜ
+# ——————————————
 examples = [
     ["datasets/test/images/WEB10432.jpg", 0.25],
     ["datasets/test/images/WEB11791.jpg", 0.25],
@@ -86,49 +108,31 @@ examples = [
 
 with gr.Blocks() as demo:
     gr.Markdown("## 🔥 Wildfire Smoke & Fire Detector")
-    gr.Markdown(
-        "Upload an image below, adjust the confidence threshold, "
-        "and the model will highlight any smoke or fire regions."
-    )
+    gr.Markdown("Upload an image, adjust confidence threshold, and detect 🔥/🚬 regions.")
 
     with gr.Row():
         with gr.Column(scale=1, min_width=200):
-            input_img   = gr.Image(type="numpy", label="Input Image")
-            conf_slider = gr.Slider(0.0, 1.0, 0.25, 0.01, label="Confidence Threshold")
-            run_btn     = gr.Button("Detect 🔍", variant="primary")
+            inp    = gr.Image(type="numpy", label="Input Image")
+            conf   = gr.Slider(0,1,0.25,0.01, label="Confidence Threshold")
+            btn    = gr.Button("Detect 🔍", variant="primary")
         with gr.Column(scale=5, min_width=1000):
-            output_img = gr.Image(
-                type="numpy",
-                label="Annotated Output",
-                height=800,
-                width=1000
-            )
-            time_txt   = gr.Textbox(label="Performance", interactive=False)
+            out_img  = gr.Image(type="numpy", label="Annotated Output", height=800, width=1000)
+            out_text = gr.Textbox(label="Status / Performance", interactive=False)
 
-    run_btn.click(
-        fn=infer_fire,
-        inputs=[input_img, conf_slider],
-        outputs=[output_img, time_txt]
-    )
+    btn.click(infer_fire, [inp, conf], [out_img, out_text])
 
     gr.Examples(
         examples=examples,
-        inputs=[input_img, conf_slider],
-        outputs=[output_img, time_txt],
+        inputs=[inp, conf],
+        outputs=[out_img, out_text],
         fn=infer_fire,
         cache_examples=False
     )
 
     gr.Markdown(
-        "---\n"
-        "Model trained on a custom wildfire dataset using YOLOv8. "
-        "Adjust the threshold to trade off between false positives and false negatives."
+        "---\nModel trained on a custom wildfire dataset using YOLOv8. "
+        "CPU’da çalışıyorsanız `device='cpu'`, GPU varsa `device=0` seçildi."
     )
 
 if __name__ == "__main__":
     demo.launch()
-       # server_name="0.0.0.0",
-       # server_port=7861,
-       # share=False,
-       # inbrowser=True
-