Update app.py

app.py

@@ -1,6 +1,8 @@
 import gradio as gr
 from PIL import Image
 import torch
+import base64
+import io
 
 from transformers import (
     pipeline,
@@ -8,9 +10,6 @@ from transformers import (
     AutoImageProcessor
 )
 
-# -------------------------
-# Model & Processor yükle
-# -------------------------
 MODEL_ID = "cagrigungor/fire-prediction"
 
 model = AutoModelForImageClassification.from_pretrained(MODEL_ID)
@@ -19,43 +18,75 @@ processor = AutoImageProcessor.from_pretrained(MODEL_ID)
 pipe = pipeline(
     task="image-classification",
     model=model,
-    image_processor=processor
+    image_processor=processor,
+    device=-1
 )
 
 # -------------------------
-# Inference fonksiyonu
+# Inference
 # -------------------------
-def predict(image: Image.Image):
+def predict_from_image(image):
     if image is None:
-        return "Lütfen bir görüntü yükleyin."
+        return None
 
     image = image.convert("RGB")
-
     results = pipe(image)
 
-    # Sonuçları okunabilir hale getir
-    output = []
-    for r in results:
-        label = r["label"]
-        score = round(r["score"] * 100, 2)
-        output.append(f"{label}: %{score}")
+    return {r["label"]: float(r["score"]) for r in results}
 
-    return "\n".join(output)
 
 # -------------------------
-# Gradio UI
+# Base64 (haritadan gelen)
 # -------------------------
-app = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Yangın Görüntüsü Yükle"),
-    outputs=gr.Textbox(label="Tahmin Sonucu"),
-    title="🔥 Wildfire Detection",
-    description="ViT tabanlı yangın tespit modeli. Görüntü yükleyerek test edebilirsiniz.",
-    allow_flagging="never"
-)
+def predict_from_base64(base64_str):
+    if base64_str is None:
+        return None
+
+    image_bytes = base64.b64decode(base64_str.split(",")[1])
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    return predict_from_image(image)
+
 
 # -------------------------
-# Run
+# UI
 # -------------------------
-if __name__ == "__main__":
-    app.launch()
+with gr.Blocks(title="🔥 Wildfire Detection with Map") as app:
+    gr.Markdown("## 🔥 Wildfire Detection (OSM + Image Upload)")
+
+    with gr.Tabs():
+        # -------------------------
+        # TAB 1: Manual Upload
+        # -------------------------
+        with gr.Tab("📤 Resim Yükle"):
+            img_input = gr.Image(type="pil", label="Görüntü Yükle")
+            btn1 = gr.Button("Tahmin Et")
+            out1 = gr.Label(num_top_classes=2)
+
+            btn1.click(
+                fn=predict_from_image,
+                inputs=img_input,
+                outputs=out1
+            )
+
+        # -------------------------
+        # TAB 2: Map
+        # -------------------------
+        with gr.Tab("🗺️ Harita (OSM)"):
+            gr.HTML("""
+            <iframe
+              src="/map.html"
+              style="width:100%; height:600px; border:none;">
+            </iframe>
+            """)
+
+            base64_input = gr.Textbox(visible=False)
+            btn2 = gr.Button("Haritadan Tahmin Et")
+            out2 = gr.Label(num_top_classes=2)
+
+            btn2.click(
+                fn=predict_from_base64,
+                inputs=base64_input,
+                outputs=out2
+            )
+
+app.launch()