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0484001 af33715 0484001 af33715 0484001 af33715 0484001 af33715 0484001 af33715 0484001 af33715 0484001 af33715 0484001 af33715 0484001 af33715 0484001 af33715 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import gradio as gr
from PIL import Image
import torch
import base64
import io
from transformers import (
pipeline,
AutoModelForImageClassification,
AutoImageProcessor
)
MODEL_ID = "cagrigungor/fire-prediction"
model = AutoModelForImageClassification.from_pretrained(MODEL_ID)
processor = AutoImageProcessor.from_pretrained(MODEL_ID)
pipe = pipeline(
task="image-classification",
model=model,
image_processor=processor,
device=-1
)
# -------------------------
# Inference
# -------------------------
def predict_from_image(image):
if image is None:
return None
image = image.convert("RGB")
results = pipe(image)
return {r["label"]: float(r["score"]) for r in results}
# -------------------------
# Base64 (haritadan gelen)
# -------------------------
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)
# -------------------------
# UI
# -------------------------
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()
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