Update app.py

app.py

@@ -1,26 +1,82 @@
+
 import gradio as gr
 import numpy as np
-import joblib
-
-# Load model (ensure this .pkl is uploaded)
-model = joblib.load("gas_classification_model.pkl")
-
-# Define feature input labels
-sensor_labels = [f"Sensor {i+1}" for i in range(10)]
-
-# Predict function
-def predict_gas(*sensor_values):
-    input_data = np.array(sensor_values).reshape(1, -1)
-    prediction = model.predict(input_data)[0]
-    return f"Predicted Gas Type: {prediction}"
-
-# Gradio UI
-iface = gr.Interface(
-    fn=predict_gas,
-    inputs=[gr.Number(label=label) for label in sensor_labels],
-    outputs=gr.Textbox(label="Gas Type"),
-    title="Gas Sensor Classifier",
-    description="Enter 10 sensor readings to classify the gas type.",
+import pickle
+
+# Load your trained model
+with open("gas_classification_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# --- Feature Extraction ---
+
+def compute_ema(series, alpha):
+    ema = 0
+    ema_values = []
+    for val in series:
+        ema = alpha * val + (1 - alpha) * ema
+        ema_values.append(ema)
+    return np.array(ema_values)
+
+def extract_features(sensor_data, baseline_range=(0, 20), exposure_range=(20, 80)):
+    features = []
+    for sensor_id in range(1, 17):
+        R = np.array(sensor_data[sensor_id])
+        baseline = np.mean(R[baseline_range[0]:baseline_range[1]])
+        exposure = R[exposure_range[0]:exposure_range[1]]
+        peak = np.max(exposure)
+        delta_R = peak - baseline
+        norm_delta_R = peak / baseline if baseline != 0 else 0
+
+        peak_index = np.argmax(exposure) + exposure_range[0]
+        rising = R[baseline_range[1]:peak_index]
+        decaying = R[peak_index:]
+
+        alphas = [0.001, 0.01, 0.1]
+        ema_rising = [np.max(compute_ema(rising, a)) for a in alphas]
+        ema_decaying = [np.min(compute_ema(decaying, a)) for a in alphas]
+
+        sensor_features = [delta_R, norm_delta_R] + ema_rising + ema_decaying
+        features.extend(sensor_features)
+
+    return np.array(features)
+
+# --- Prediction Pipeline ---
+
+def predict(sensor1, sensor2, sensor3, sensor4, sensor5, sensor6, sensor7, sensor8,
+            sensor9, sensor10, sensor11, sensor12, sensor13, sensor14, sensor15, sensor16):
+    
+    # Convert all inputs to float lists
+    sensors = [sensor1, sensor2, sensor3, sensor4, sensor5, sensor6, sensor7, sensor8,
+               sensor9, sensor10, sensor11, sensor12, sensor13, sensor14, sensor15, sensor16]
+    
+    sensor_data = {}
+    for i in range(16):
+        try:
+            values = [float(x.strip()) for x in sensors[i].split(",")]
+            if len(values) < 100:
+                return f"Sensor {i+1} must have at least 100 values."
+            sensor_data[i+1] = values
+        except:
+            return f"Invalid input format for Sensor {i+1}. Use comma-separated numbers."
+
+    features = extract_features(sensor_data)
+    prediction = model.predict([features])[0]
+    return f"🚨 Predicted Gas Type: {prediction}"
+
+# --- Gradio UI ---
+
+sensor_inputs = [
+    gr.Textbox(label=f"Sensor {i+1} Readings (comma-separated, 100+ values)", lines=2, placeholder="e.g. 1.01, 1.02, 1.03, ...")
+    for i in range(16)
+]
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=sensor_inputs,
+    outputs=gr.Text(label="Prediction"),
+    title="MOS Sensor Gas Prediction",
+    description="Paste comma-separated time-series readings (100+ values) from each of the 16 sensors to get a gas type prediction."
 )
 
-iface.launch()
+if __name__ == "__main__":
+    demo.launch()