Update app.py

app.py

@@ -1,8 +1,94 @@
+# import numpy as np
+# import gradio as gr
+# from tensorflow.keras.models import load_model, Model
+# from hpelm import ELM
+# import joblib
+
+# # 🔄 Load scaler and ELM model
+# scaler = joblib.load("elm_scaler1.pkl")
+# elm = ELM(128, 1, classification='c')
+# elm.load("elm_model1.txt")
+
+# # ⚙️ Load trained MobileNet1D and create feature extractor
+# mobilenet = load_model("physio_model2.h5")
+# feature_extractor = Model(inputs=mobilenet.input, outputs=mobilenet.get_layer("penultimate_dense").output)
+
+# # 🔍 Preprocessing
+# def preprocess_signal(ecg, eda, temp):
+#     ecg = np.array(ecg).reshape(-1, 1)
+#     eda = np.array(eda).reshape(-1, 1)
+#     temp = np.array(temp).reshape(-1, 1)
+#     return np.concatenate([ecg, eda, temp], axis=1)
+
+# # 🔮 Prediction
+# def predict_stress(ecg, eda, temp):
+#     try:
+#         signal = preprocess_signal(ecg, eda, temp).reshape(1, 1280, 3)
+#         features = feature_extractor.predict(signal)
+#         features_scaled = scaler.transform(features)
+#         raw_pred = elm.predict(features_scaled)
+#         raw_pred = np.clip(raw_pred, -20, 20)
+#         prob = 1 / (1 + np.exp(-raw_pred))
+#         label = "Stress" if prob > 0.5 else "No Stress"
+#         return f"{label} (Confidence: {prob[0][0]:.2f})"
+#     except Exception as e:
+#         return f"❌ Error: {str(e)}"
+
+# # 🧪 Parser for string inputs
+# def parse_and_predict(ecg_str, eda_str, temp_str):
+#     try:
+#         ecg = list(map(float, ecg_str.strip().split(',')))
+#         eda = list(map(float, eda_str.strip().split(',')))
+#         temp = list(map(float, temp_str.strip().split(',')))
+#         if len(ecg) != 1280 or len(eda) != 1280 or len(temp) != 1280:
+#             return "❌ Each signal must be exactly 1280 samples long."
+#         return predict_stress(ecg, eda, temp)
+#     except:
+#         return "❌ Invalid input format. Use comma-separated float values."
+
+# # 📊 Demo stress data
+# def generate_stress_sample():
+#     t = np.linspace(0, 10, 1280)
+#     ecg = 0.6 * np.sin(2 * np.pi * 1.8 * t) + 0.1 * np.random.randn(1280)
+#     eda = np.linspace(0.5, 1.2, 1280) + 0.05 * np.random.randn(1280)
+#     eda += np.random.choice([0, 0.3], size=1280, p=[0.95, 0.05])
+#     temp = 36.2 + 0.01 * np.random.randn(1280) - 0.05 * np.linspace(0, 1, 1280)
+#     return ecg, eda, temp
+
+# # 👇 Separate into text strings
+# ecg_demo, eda_demo, temp_demo = generate_stress_sample()
+# demo_example = [
+#     ','.join([str(round(v, 4)) for v in ecg_demo]),
+#     ','.join([str(round(v, 4)) for v in eda_demo]),
+#     ','.join([str(round(v, 4)) for v in temp_demo])
+# ]
+
+# # 🎛️ Gradio Interface
+# iface = gr.Interface(
+#     fn=parse_and_predict,
+#     inputs=[
+#         gr.Textbox(label="ECG Signal (1280 values, comma-separated)", lines=4),
+#         gr.Textbox(label="EDA Signal (1280 values, comma-separated)", lines=4),
+#         gr.Textbox(label="Temperature Signal (1280 values, comma-separated)", lines=4)
+#     ],
+#     outputs=gr.Textbox(label="Prediction"),
+#     examples=[demo_example],
+#     title="🧠 Stress Detection from Physiological Signals",
+#     description="Enter 1280 comma-separated ECG, EDA, and Temp signal values. The model will classify stress vs no stress."
+# )
+
+# # 🏁 Launch
+# if __name__ == "__main__":
+#     iface.launch()
+
+
 import numpy as np
+import pandas as pd
 import gradio as gr
 from tensorflow.keras.models import load_model, Model
 from hpelm import ELM
 import joblib
+import tempfile
 
 # 🔄 Load scaler and ELM model
 scaler = joblib.load("elm_scaler1.pkl")
@@ -13,70 +99,88 @@ elm.load("elm_model1.txt")
 mobilenet = load_model("physio_model2.h5")
 feature_extractor = Model(inputs=mobilenet.input, outputs=mobilenet.get_layer("penultimate_dense").output)
 
-# 🔍 Preprocessing
 def preprocess_signal(ecg, eda, temp):
     ecg = np.array(ecg).reshape(-1, 1)
     eda = np.array(eda).reshape(-1, 1)
     temp = np.array(temp).reshape(-1, 1)
     return np.concatenate([ecg, eda, temp], axis=1)
 
-# 🔮 Prediction
 def predict_stress(ecg, eda, temp):
     try:
-        signal = preprocess_signal(ecg, eda, temp).reshape(1, 1280, 3)
+        signal = preprocess_signal(ecg, eda, temp)  # Shape: (1280, 3)
+        signal = signal.reshape(1, 1280, 3)
+
+        # 🔍 Feature Extraction
         features = feature_extractor.predict(signal)
+
+        # 🔄 Scaling for ELM
         features_scaled = scaler.transform(features)
+
+        # 🔮 ELM Inference
         raw_pred = elm.predict(features_scaled)
         raw_pred = np.clip(raw_pred, -20, 20)
         prob = 1 / (1 + np.exp(-raw_pred))
         label = "Stress" if prob > 0.5 else "No Stress"
+
         return f"{label} (Confidence: {prob[0][0]:.2f})"
+    
     except Exception as e:
         return f"❌ Error: {str(e)}"
 
-# 🧪 Parser for string inputs
-def parse_and_predict(ecg_str, eda_str, temp_str):
+# ✅ Predict from CSV File
+def predict_from_csv(file_obj):
     try:
-        ecg = list(map(float, ecg_str.strip().split(',')))
-        eda = list(map(float, eda_str.strip().split(',')))
-        temp = list(map(float, temp_str.strip().split(',')))
-        if len(ecg) != 1280 or len(eda) != 1280 or len(temp) != 1280:
-            return "❌ Each signal must be exactly 1280 samples long."
+        df = pd.read_csv(file_obj.name)
+        if df.shape != (1280, 3) or list(df.columns) != ["ECG", "EDA", "Temp"]:
+            return "❌ CSV must have shape (1280, 3) with columns: ECG, EDA, Temp"
+        ecg = df["ECG"].values
+        eda = df["EDA"].values
+        temp = df["Temp"].values
         return predict_stress(ecg, eda, temp)
-    except:
-        return "❌ Invalid input format. Use comma-separated float values."
+    except Exception as e:
+        return f"❌ Failed to read CSV: {str(e)}"
 
-# 📊 Demo stress data
+# ✅ Generate deterministic synthetic stress signal (not random)
 def generate_stress_sample():
-    t = np.linspace(0, 10, 1280)
-    ecg = 0.6 * np.sin(2 * np.pi * 1.8 * t) + 0.1 * np.random.randn(1280)
-    eda = np.linspace(0.5, 1.2, 1280) + 0.05 * np.random.randn(1280)
-    eda += np.random.choice([0, 0.3], size=1280, p=[0.95, 0.05])
-    temp = 36.2 + 0.01 * np.random.randn(1280) - 0.05 * np.linspace(0, 1, 1280)
-    return ecg, eda, temp
-
-# 👇 Separate into text strings
-ecg_demo, eda_demo, temp_demo = generate_stress_sample()
-demo_example = [
-    ','.join([str(round(v, 4)) for v in ecg_demo]),
-    ','.join([str(round(v, 4)) for v in eda_demo]),
-    ','.join([str(round(v, 4)) for v in temp_demo])
-]
-
-# 🎛️ Gradio Interface
-iface = gr.Interface(
-    fn=parse_and_predict,
-    inputs=[
-        gr.Textbox(label="ECG Signal (1280 values, comma-separated)", lines=4),
-        gr.Textbox(label="EDA Signal (1280 values, comma-separated)", lines=4),
-        gr.Textbox(label="Temperature Signal (1280 values, comma-separated)", lines=4)
-    ],
-    outputs=gr.Textbox(label="Prediction"),
-    examples=[demo_example],
-    title="🧠 Stress Detection from Physiological Signals",
-    description="Enter 1280 comma-separated ECG, EDA, and Temp signal values. The model will classify stress vs no stress."
-)
+    timesteps = 1280
+    t = np.linspace(0, 10, timesteps)
+
+    # Simulated stress ECG: High-frequency sinusoid
+    ecg = 0.6 * np.sin(2 * np.pi * 1.8 * t)
+
+    # Simulated EDA: Linearly increasing with occasional burst
+    eda = 0.5 + 0.7 * (t / t.max())
+    eda[::256] += 0.3  # Small spikes
+
+    # Simulated Temp: Slight downward trend
+    temp = 36.4 - 0.05 * (t / t.max())
+
+    return np.stack([ecg, eda, temp], axis=1)
+
+def generate_stress_sample_csv():
+    sample = generate_stress_sample()
+    df = pd.DataFrame(sample, columns=["ECG", "EDA", "Temp"])
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+    df.to_csv(tmp.name, index=False)
+    return tmp.name
+
+# ✅ Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧠 Stress Detection from Physiological Signals")
+    gr.Markdown("Upload a CSV file with 1280 rows and 3 columns: **ECG**, **EDA**, **Temp**")
+
+    with gr.Row():
+        file_input = gr.File(label="📁 Upload CSV file", file_types=[".csv"])
+        output = gr.Textbox(label="Prediction")
+
+    file_input.change(fn=predict_from_csv, inputs=file_input, outputs=output)
+
+    gr.Markdown("### Or generate a sample stressed input:")
+    generate_btn = gr.Button("Generate Stress Sample CSV")
+    demo_file = gr.File(label="⬇️ Download sample and re-upload above")
+
+    generate_btn.click(fn=generate_stress_sample_csv, outputs=demo_file)
 
 # 🏁 Launch
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True, show_api=True)