prithivMLmods/Forest-Fire-Detection

Classification Report:
              precision    recall  f1-score   support

        Fire     0.9960    0.9896    0.9928      2020
      Normal     0.9902    0.9960    0.9931      2020
       Smoke     0.9995    1.0000    0.9998      2020

    accuracy                         0.9952      6060
   macro avg     0.9952    0.9952    0.9952      6060
weighted avg     0.9952    0.9952    0.9952      6060

Class 0: Fire  
Class 1: Normal  
Class 2: Smoke

pip install -q transformers torch pillow gradio hf_xet

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Forest-Fire-Detection"  # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Updated label mapping
id2label = {
    "0": "Fire",
    "1": "Normal",
    "2": "Smoke"
}

def classify_image(image):
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    prediction = {
        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
    }

    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=3, label="Forest Fire Detection"),
    title="Forest-Fire-Detection",
    description="Upload an image to detect whether the scene contains fire, smoke, or is normal."
)

if __name__ == "__main__":
    iface.launch()