first

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+fire_detection_lstm_model.keras filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,14 @@
+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+WORKDIR /app
+
+COPY requirements.txt /app/
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . /app/
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,52 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import numpy as np
+import joblib
+from tensorflow.keras.models import load_model
+from typing import List
+
+# Initialize app
+app = FastAPI(title="🔥 Fire Source Classification API")
+
+# Load model and preprocessing tools
+model = load_model("fire_detection_lstm_model.keras")
+scaler = joblib.load("scaler.pkl")
+label_encoder = joblib.load("label_encoder.pkl")
+
+# Request schema
+class SensorInput(BaseModel):
+    window: List[List[float]]  # List of 30 time steps, each with 5 sensor values
+
+@app.get("/")
+def root():
+    return {"message": "Fire source classification model is ready!"}
+
+@app.post("/predict")
+def predict_fire_type(data: SensorInput):
+    sensor_window = np.array(data.window)
+
+    # Validate shape
+    if sensor_window.shape != (30, 5):
+        raise HTTPException(status_code=400, detail="Input must be a 30x5 list of floats (30 time steps, 5 features).")
+
+    # Scale and reshape
+    try:
+        sensor_window_scaled = scaler.transform(sensor_window)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Scaling failed: {str(e)}")
+
+    sensor_input = sensor_window_scaled.reshape(1, 30, 5)
+
+    # Predict
+    try:
+        probs = model.predict(sensor_input)
+        predicted_index = int(np.argmax(probs, axis=1)[0])
+        predicted_class = label_encoder.inverse_transform([predicted_index])[0]
+        confidence = float(np.max(probs))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction failed: {str(e)}")
+
+    return {
+        "predicted_class": predicted_class,
+        "confidence": round(confidence, 4)
+    }

fire_detection_lstm_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2ea0e41fac5e2ed3d5ab0dfac52c59b42d6957c7c93fe3ce1ca439e033d074b
+size 1512377

label_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb205f4189abb57de2ff58126e8b58060605febdc5a4d254ccccfe528ecadac9
+size 577

requirements.txt

@@ -0,0 +1,7 @@
+fastapi
+uvicorn
+numpy
+scikit-learn
+tensorflow
+joblib
+pydantic

scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53e90403fab69862494ff6dcf413fe7769dee7a3bda4f73c03ccc853fe70eaf3
+size 719