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lstm_cnn_app.py

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+"""
+lstm_cnn_app.py
+Gradio app to serve the CNN-LSTM fault classification model.
+
+Usage:
+- Place a local model file named by LOCAL_MODEL_FILE in the same repo, or
+- Set HUB_REPO and HUB_FILENAME to a public Hugging Face model repo + filename,
+  and the app will download it at startup using hf_hub_download.
+"""
+import os
+import numpy as np
+import pandas as pd
+import gradio as gr
+from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
+
+# CONFIG: change these if your model filename/repo are different
+LOCAL_MODEL_FILE = "lstm_cnn_model.h5"
+HUB_REPO = ""         # e.g., "username/lstm-cnn-model"
+HUB_FILENAME = ""     # e.g., "lstm_cnn_model.h5"
+
+def get_model_path():
+    if os.path.exists(LOCAL_MODEL_FILE):
+        return LOCAL_MODEL_FILE
+    if HUB_REPO and HUB_FILENAME:
+        try:
+            print(f"Downloading {HUB_FILENAME} from {HUB_REPO} ...")
+            return hf_hub_download(repo_id=HUB_REPO, filename=HUB_FILENAME)
+        except Exception as e:
+            print("Failed to download from hub:", e)
+    return None
+
+MODEL_PATH = get_model_path()
+MODEL = None
+if MODEL_PATH:
+    try:
+        MODEL = load_model(MODEL_PATH)
+        print("Loaded model:", MODEL_PATH)
+    except Exception as e:
+        print("Failed to load model:", e)
+        MODEL = None
+else:
+    print("No model found. Please upload a model named", LOCAL_MODEL_FILE, "or set HUB_REPO/HUB_FILENAME.")
+
+def prepare_input_array(arr, n_timesteps=1, n_features=None):
+    arr = np.array(arr)
+    if arr.ndim == 1:
+        if n_features is None:
+            return arr.reshape(1, n_timesteps, -1)
+        return arr.reshape(1, n_timesteps, n_features)
+    elif arr.ndim == 2:
+        return arr
+    else:
+        return arr
+
+def predict_text(text, n_timesteps=1, n_features=None):
+    if MODEL is None:
+        return "模型未加载，请上传或配置模型。"
+    arr = np.fromstring(text, sep=',')
+    x = prepare_input_array(arr, n_timesteps=int(n_timesteps), n_features=(int(n_features) if n_features else None))
+    probs = MODEL.predict(x)
+    label = int(np.argmax(probs, axis=1)[0])
+    return f"预测类别: {label} (概率: {float(np.max(probs)):.4f})"
+
+def predict_csv(file, n_timesteps=1, n_features=None):
+    if MODEL is None:
+        return {"error": "模型未加载，请上传或配置模型。"}
+    df = pd.read_csv(file.name)
+    X = df.values
+    if n_features:
+        X = X.reshape(X.shape[0], int(n_timesteps), int(n_features))
+    preds = MODEL.predict(X)
+    labels = preds.argmax(axis=1).tolist()
+    return {"labels": labels, "probs": preds.tolist()}
+
+with gr.Blocks() as demo:
+    gr.Markdown("# CNN-LSTM Fault Classification")
+    gr.Markdown("上传 CSV（每行一个样本）或粘贴逗号分隔的一行特征进行预测。")
+    with gr.Row():
+        file_in = gr.File(label="上传 CSV（每行 = 一个样本）")
+        text_in = gr.Textbox(lines=2, placeholder="粘贴逗号分隔的一行特征，例如: 0.1,0.2,0.3,...")
+    n_ts = gr.Number(value=1, label="timesteps (整型)")
+    n_feat = gr.Number(value=None, label="features (可选，留空尝试自动推断)")
+    btn = gr.Button("预测")
+    out_text = gr.Textbox(label="单样本预测输出")
+    out_json = gr.JSON(label="批量预测结果 (labels & probs)")
+
+    def run_predict(file, text, n_timesteps, n_features):
+        if file is not None:
+            return "CSV 预测完成", predict_csv(file, n_timesteps, n_features)
+        if text:
+            return predict_text(text, n_timesteps, n_features), {}
+        return "请提供 CSV 或特征文本", {}
+
+    btn.click(run_predict, inputs=[file_in, text_in, n_ts, n_feat], outputs=[out_text, out_json])
+
+if __name__ == '__main__':
+    demo.launch(server_name='0.0.0.0', server_port=7861)

requirements.txt

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+gradio>=3.0
+tensorflow>=2.6
+numpy
+pandas
+scikit-learn
+huggingface_hub
+matplotlib

tcn_app.py

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+"""
+tcn_app.py
+Gradio app to serve the TCN fault classification model.
+
+Usage:
+- Place a local model file named by LOCAL_MODEL_FILE in the same repo, or
+- Set HUB_REPO and HUB_FILENAME to a public Hugging Face model repo + filename,
+  and the app will download it at startup using hf_hub_download.
+
+This file is ready to push to a Hugging Face Space (Gradio).
+"""
+import os
+import numpy as np
+import pandas as pd
+import gradio as gr
+from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
+
+# CONFIG: change these if your model filename/repo are different
+LOCAL_MODEL_FILE = "tcn_model.h5"
+HUB_REPO = ""         # e.g., "username/tcn-model-repo" (leave empty to disable)
+HUB_FILENAME = ""     # e.g., "tcn_model.h5"
+
+def get_model_path():
+    # prefer local
+    if os.path.exists(LOCAL_MODEL_FILE):
+        return LOCAL_MODEL_FILE
+    # try hub
+    if HUB_REPO and HUB_FILENAME:
+        try:
+            print(f"Downloading {HUB_FILENAME} from {HUB_REPO} ...")
+            return hf_hub_download(repo_id=HUB_REPO, filename=HUB_FILENAME)
+        except Exception as e:
+            print("Failed to download from hub:", e)
+    return None
+
+MODEL_PATH = get_model_path()
+MODEL = None
+if MODEL_PATH:
+    try:
+        MODEL = load_model(MODEL_PATH)
+        print("Loaded model:", MODEL_PATH)
+    except Exception as e:
+        print("Failed to load model:", e)
+        MODEL = None
+else:
+    print("No model found. Please upload a model named", LOCAL_MODEL_FILE, "or set HUB_REPO/HUB_FILENAME.")
+
+def prepare_input_array(arr, n_timesteps=1, n_features=None):
+    arr = np.array(arr)
+    # If input is 1D, reshape to (1, n_timesteps, n_features)
+    if arr.ndim == 1:
+        if n_features is None:
+            # if user didn't supply n_features, assume arr is already shaped as (timesteps*features,)
+            return arr.reshape(1, n_timesteps, -1)
+        return arr.reshape(1, n_timesteps, n_features)
+    elif arr.ndim == 2:
+        # Already (timesteps, features) or (samples, features)
+        if arr.shape[0] == 1:
+            return arr.reshape(1, arr.shape[1], -1)
+        return arr
+    else:
+        return arr
+
+def predict_text(text, n_timesteps=1, n_features=None):
+    if MODEL is None:
+        return "模型未加载，请上传或配置模型。"
+    arr = np.fromstring(text, sep=',')
+    x = prepare_input_array(arr, n_timesteps=int(n_timesteps), n_features=(int(n_features) if n_features else None))
+    probs = MODEL.predict(x)
+    label = int(np.argmax(probs, axis=1)[0])
+    return f"预测类别: {label} (概率: {float(np.max(probs)):.4f})"
+
+def predict_csv(file, n_timesteps=1, n_features=None):
+    if MODEL is None:
+        return {"error": "模型未加载，请上传或配置模型。"}
+    df = pd.read_csv(file.name)
+    X = df.values
+    if n_features:
+        X = X.reshape(X.shape[0], int(n_timesteps), int(n_features))
+    preds = MODEL.predict(X)
+    labels = preds.argmax(axis=1).tolist()
+    return {"labels": labels, "probs": preds.tolist()}
+
+with gr.Blocks() as demo:
+    gr.Markdown("# TCN Fault Classification")
+    gr.Markdown("上传 CSV（每行一个样本）或粘贴逗号分隔的一行特征进行预测。")
+    with gr.Row():
+        file_in = gr.File(label="上传 CSV（每行 = 一个样本）")
+        text_in = gr.Textbox(lines=2, placeholder="粘贴逗号分隔的一行特征，例如: 0.1,0.2,0.3,...")
+    n_ts = gr.Number(value=1, label="timesteps (整型)")
+    n_feat = gr.Number(value=None, label="features (可选，留空尝试自动推断)")
+    btn = gr.Button("预测")
+    out_text = gr.Textbox(label="单样本预测输出")
+    out_json = gr.JSON(label="批量预测结果 (labels & probs)")
+
+    def run_predict(file, text, n_timesteps, n_features):
+        if file is not None:
+            return "CSV 预测完成", predict_csv(file, n_timesteps, n_features)
+        if text:
+            return predict_text(text, n_timesteps, n_features), {}
+        return "请提供 CSV 或特征文本", {}
+
+    btn.click(run_predict, inputs=[file_in, text_in, n_ts, n_feat], outputs=[out_text, out_json])
+
+if __name__ == '__main__':
+    demo.launch(server_name='0.0.0.0', server_port=7860)