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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
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+Images/DT.png filter=lfs diff=lfs merge=lfs -text
+Images/Liner_Regresion.png filter=lfs diff=lfs merge=lfs -text
+Images/LSTM.png filter=lfs diff=lfs merge=lfs -text
+Images/Random_Forest.png filter=lfs diff=lfs merge=lfs -text

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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"101_T8xMCWHcohzgIs8NvdK3wYK7h7lwO","authorship_tag":"ABX9TyPqWv0VJNnuZXYPzsYolcuK"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["!pip install gradio"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oL1tjvMuzso2","executionInfo":{"status":"ok","timestamp":1745179514537,"user_tz":-180,"elapsed":11973,"user":{"displayName":"مالك المصنف","userId":"01645315346138749579"}},"outputId":"69f9431f-742c-42ce-e0de-a81909f4d23b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting gradio\n","  Downloading gradio-5.25.2-py3-none-any.whl.metadata (16 kB)\n","Collecting aiofiles<25.0,>=22.0 (from gradio)\n","  Downloading aiofiles-24.1.0-py3-none-any.whl.metadata (10 kB)\n","Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.11/dist-packages (from 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uvicorn-0.34.2\n"]}]},{"cell_type":"code","source":["\n","import gradio as gr\n","import pandas as pd\n","import pickle\n","import numpy as np\n","from tensorflow.keras.models import load_model\n","\n","# Load models and scalers\n","with open(\"/content/drive/MyDrive/Analaysis for Time Fauiler/Models/Feature_Scaler.pkl\", \"rb\") as f:\n","    scaler = pickle.load(f)\n","with open(\"/content/drive/MyDrive/Analaysis for Time Fauiler/Models/target_scaler.pkl\", \"rb\") as f:\n","    target_scaler = pickle.load(f)\n","with open(\"/content/drive/MyDrive/Analaysis for Time Fauiler/Models/LR_model.pkl\", \"rb\") as f:\n","    linear_model = pickle.load(f)\n","with open(\"/content/drive/MyDrive/Analaysis for Time Fauiler/Models/DT_model.pkl\", \"rb\") as f:\n","    dt_model = pickle.load(f)\n","with open(\"/content/drive/MyDrive/Analaysis for Time Fauiler/Models/RF_model.pkl\", \"rb\") as f:\n","    rf_model = pickle.load(f)\n","lstm_model = load_model(\"/content/drive/MyDrive/Analaysis for Time Fauiler/Models/best_model.h5\")\n","\n","# Create sequences for LSTM\n","def create_sequences(data, window_size=11):\n","    sequences = []\n","    for i in range(len(data) - window_size + 1):\n","        seq = data[i:i+window_size]\n","        sequences.append(seq)\n","    return np.array(sequences).astype('float32')\n","\n","# Data processing and alert function\n","def process_and_alert(file):\n","    try:\n","        df = pd.read_csv(file.name)\n","        df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n","        df = df.sort_values(\"timestamp\").reset_index(drop=True)\n","        df[\"fault_flag\"] = df[\"status\"].apply(lambda x: 1 if x == \"fault\" else 0)\n","        fault_indices = df[df[\"fault_flag\"] == 1].index.tolist()\n","        time_to_failure = []\n","        for i in range(len(df)):\n","            next_faults = [j for j in fault_indices if j >= i]\n","            if next_faults:\n","                seconds = (df.loc[next_faults[0], \"timestamp\"] - df.loc[i, \"timestamp\"]).total_seconds()\n","            else:\n","                seconds = None\n","            time_to_failure.append(seconds)\n","        df[\"time_to_failure\"] = time_to_failure\n","        df.dropna(inplace=True)\n","        X = df.drop(columns=['time_to_failure', 'fault_flag', 'status', 'timestamp'])\n","        X_scaled = scaler.transform(X)\n","\n","        # LSTM processing\n","        window_size = 11\n","        if len(X_scaled) < window_size:\n","            raise ValueError(f\"Model requires at least {window_size} samples!\")\n","        X_seq = create_sequences(X_scaled, window_size)\n","\n","        # Predictions\n","        pred_linear = linear_model.predict(X_scaled)\n","        pred_dt = dt_model.predict(X_scaled)\n","        pred_rf = rf_model.predict(X_scaled)\n","        pred_lstm = lstm_model.predict(X_seq)\n","\n","        # Align lengths\n","        min_length = min(len(pred_linear), len(pred_dt), len(pred_rf), len(pred_lstm))\n","        pred_linear = pred_linear[:min_length]\n","        pred_dt = pred_dt[:min_length]\n","        pred_rf = pred_rf[:min_length]\n","        pred_lstm = pred_lstm[:min_length]\n","\n","        # Inverse transform\n","        pred_lstm = target_scaler.inverse_transform(pred_lstm.reshape(-1, 1))\n","        pred_linear = target_scaler.inverse_transform(pred_linear.reshape(-1, 1))\n","        pred_dt = target_scaler.inverse_transform(pred_dt.reshape(-1, 1))\n","        pred_rf = target_scaler.inverse_transform(pred_rf.reshape(-1, 1))\n","\n","        def format_value(val):\n","            return f'<span style=\"color:red;font-weight:bold;\">{val:.2f} (Fault)</span>' if val < 0 else f'{val:.2f}'\n","\n","        html_rows = \"\"\n","        for i in range(min_length):\n","            html_rows += \"<tr>\" + \"\".join([\n","                f\"<td>{format_value(pred_linear[i][0])}-second</td>\",\n","                f\"<td>{format_value(pred_dt[i][0])}-second</td>\",\n","                f\"<td>{format_value(pred_rf[i][0])}-second</td>\",\n","                f\"<td>{format_value(pred_lstm[i][0])}-second</td>\"\n","            ]) + \"</tr>\"\n","\n","        html_table = f\"\"\"\n","        <table border=\"1\" style=\"border-collapse:collapse; width:100%; text-align:center;\">\n","            <thead>\n","                <tr style=\"background-color:#f0f0f0;\">\n","                    <th>Linear Regression</th>\n","                    <th>Decision Tree</th>\n","                    <th>Random Forest</th>\n","                    <th>LSTM</th>\n","                </tr>\n","            </thead>\n","            <tbody>\n","                {html_rows}\n","            </tbody>\n","        </table>\n","        \"\"\"\n","\n","        # Alert System\n","        preds = {\n","            \"Linear Regression\": pred_linear,\n","            \"Decision Tree\": pred_dt,\n","            \"Random Forest\": pred_rf,\n","            \"LSTM\": pred_lstm\n","        }\n","        alerts = []\n","        for model_name, values in preds.items():\n","            positives = values[values > 0]\n","            if positives.size > 0:\n","                min_pos = np.min(positives)\n","                alerts.append((model_name, min_pos))\n","        if not alerts:\n","            alert_msg = \"<h3 style='color:red;'>❌ No positive predictions found! Failure may have already occurred.</h3>\"\n","        else:\n","            alerts.sort(key=lambda x: x[1])\n","            best_model, time_left = alerts[0]\n","            minutes = int(time_left // 60)\n","            seconds = int(time_left % 60)\n","            color = \"red\" if time_left < 60 else \"orange\" if time_left < 180 else \"green\"\n","            msg = f\"{minutes} minute(s) and {seconds} second(s)\" if minutes > 0 else f\"{seconds} second(s)\"\n","            alert_msg = f\"\"\"\n","            <div style=\"padding:20px; border:2px solid {color}; border-radius:10px;\">\n","                <h3>🔔 Failure Alert</h3>\n","                <p><strong>Model:</strong> <span style=\"color:blue;\">{best_model}</span></p>\n","                <p><strong>Estimated time to failure:</strong> <span style=\"color:{color}; font-weight:bold;\">{msg}</span></p>\n","                {\"<p style='color:red; font-weight:bold;'>⚠️ Imminent failure!</p>\" if time_left < 60 else \"\"}\n","            </div>\n","            \"\"\"\n","        return html_table, alert_msg\n","    except Exception as e:\n","        error_msg = f\"<div style='color:red; padding:20px; border:2px solid red;'><h3>❌ Error:</h3><p>{str(e)}</p></div>\"\n","        return error_msg, \"\"\n","\n","# Load comparison tables\n","def load_metrics():\n","    return pd.read_csv(\"/content/drive/MyDrive/Analaysis for Time Fauiler/Models_Metrices.csv\")\n","\n","def load_comparison():\n","    return pd.read_csv(\"/content/drive/MyDrive/Analaysis for Time Fauiler/model_comparison_20250419_0955.csv\")\n","\n","# Gradio UI\n","with gr.Blocks(title=\"📊 Model Comparison Dashboard\") as interface:\n","    with gr.Tab(\"📈 Model Comparison\"):\n","        gr.Markdown(\"## 🔍 Actual vs Predicted\")\n","        with gr.Row():\n","            gr.Image(value=\"/content/drive/MyDrive/Analaysis for Time Fauiler/Images/Liner_Regresion.png\", label=\"Linear Regression\")\n","            gr.Image(value=\"/content/drive/MyDrive/Analaysis for Time Fauiler/Images/DT.png\", label=\"Decision Tree\")\n","        with gr.Row():\n","            gr.Image(value=\"/content/drive/MyDrive/Analaysis for Time Fauiler/Images/Random_Forest.png\", label=\"Random Forest\")\n","            gr.Image(value=\"/content/drive/MyDrive/Analaysis for Time Fauiler/Images/LSTM.png\", label=\"LSTM\")\n","        gr.Markdown(\"### 🧮 Data Distribution\")\n","        gr.Image(value=\"/content/drive/MyDrive/Analaysis for Time Fauiler/Images/Data_Dis.png\", label=\"Data Distribution\")\n","        gr.Markdown(\"### 📋 Model Metrics Table\")\n","        gr.Dataframe(load_metrics, interactive=False)\n","        gr.Markdown(\"### 🆕 Best and Worst Models Table\")\n","        gr.Dataframe(load_comparison, interactive=False)\n","\n","    with gr.Tab(\"📁 Upload Data\"):\n","        gr.Markdown(\"## 📥 Upload a new CSV file to analyze and detect failure\")\n","        file_input = gr.File(label=\"Choose CSV File\")\n","        output_html = gr.HTML(label=\"Prediction Results\")\n","        alert_output = gr.HTML(label=\"🔔 Alert\")\n","        file_input.change(fn=process_and_alert, inputs=file_input, outputs=[output_html, alert_output])\n","\n","    with gr.Tab(\"🧮 Manual Input\"):\n","        gr.Markdown(\"\"\"\n","            <div style=\"display:flex; align-items:center; gap:10px;\">\n","                <span style=\"font-size:30px;\">🧾</span>\n","                <h3 style=\"margin:0;\">Enter 3 features to get a prediction and failure alert</h3>\n","            </div>\n","        \"\"\")\n","        with gr.Row():\n","            f1 = gr.Number(label=\"vibration\")\n","            f2 = gr.Number(label=\"temperature\")\n","            f3 = gr.Number(label=\"pressure\")\n","        result_output = gr.Textbox(label=\"🔍 Predicted Time (seconds)\", interactive=False)\n","        alert_output_ready = gr.HTML(label=\"🚨 Alert\")\n","\n","        def predict_and_alert_ready(x1, x2, x3):\n","            try:\n","                X_input = np.array([[x1, x2, x3]])\n","                X_scaled = scaler.transform(X_input)\n","                pred = rf_model.predict(X_scaled).reshape(-1, 1)\n","                pred_original = target_scaler.inverse_transform(pred)[0][0]\n","                minutes = int(pred_original // 60)\n","                seconds = int(pred_original % 60)\n","                color = \"red\" if pred_original < 60 else \"orange\" if pred_original < 180 else \"green\"\n","                msg = f\"{minutes} minute(s) and {seconds} second(s)\" if minutes > 0 else f\"{seconds} second(s)\"\n","                alert_html = f\"\"\"\n","                <div style=\"padding:15px; border:2px solid {color}; border-radius:10px;\">\n","                    <h3>📢 Advanced Alert</h3>\n","                    <p><strong>Estimated time to failure:</strong> <span style=\"color:{color}; font-weight:bold;\">{msg}</span></p>\n","                    {\"<p style='color:red; font-weight:bold;'>⚠️ Imminent failure!</p>\" if pred_original < 60 else \"\"}\n","                </div>\n","                \"\"\"\n","                return f\"{pred_original:.2f} seconds\", alert_html\n","            except Exception as e:\n","                return \"Input Error\", f\"<p style='color:red;'>❌ {str(e)}</p>\"\n","\n","        btn = gr.Button(\"🔍 Predict Now\")\n","        btn.click(predict_and_alert_ready, inputs=[f1, f2, f3], outputs=[result_output, alert_output_ready])\n","\n","interface.launch(share=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":628},"id":"MKfTdIejfZIG","executionInfo":{"status":"ok","timestamp":1745180746931,"user_tz":-180,"elapsed":2519,"user":{"displayName":"مالك المصنف","userId":"01645315346138749579"}},"outputId":"d9d2d2e3-7ec3-4fba-e2e3-4d78d3fafafa"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"output_type":"stream","name":"stdout","text":["Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","* Running on public URL: https://0d8f523b48e5ed5d1f.gradio.live\n","\n","This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<div><iframe src=\"https://0d8f523b48e5ed5d1f.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":7}]}]}

Data/Models_Metrices.csv

@@ -0,0 +1,5 @@
+,Model,MAE,RMSE,R²
+0,Linear Regression,0.5982439510843085,0.7946165156709485,0.17155066451323697
+1,Decision Tree,0.6012830132868977,0.8204766899608554,0.1167507580913204
+2,Random Forest,0.5828436463410006,0.8034027199386108,0.15312877905550182
+3,LSTM,2431.832872099035,3165.383614086637,-0.06490749197841938

Data/model_comparison_20250419_0955.csv

@@ -0,0 +1,5 @@
+Model,Best Prediction,Error in Best Prediction,Worst Prediction,Error in Worst Prediction,Time of Best Prediction,Time of Worst Prediction
+Random Forest,-0.00,0.00,3267.32,10532.68,2025-04-01 8:50:00,2025-04-01 21:20:00
+LSTM,3042.28,42.28,3184.45,10615.55,2025-04-01 22:20:00,2025-04-01 19:10:00
+Linear Regression,6.43,6.43,3525.86,10274.14,2025-04-01 19:40:00,2025-04-01 21:20:00
+Decision Tree Regressor,-0.00,0.00,2344.19,11455.81,2025-04-01 8:50:00,2025-04-01 21:20:00

Models/DT_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de06789cb3b31c7c50ba4155a4172e1266a2faeedbc07bab9fb1cce48aaca87a
+size 3429

Models/Feature_Scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f241225b5176541fae29b53151db22bb488ec9babb6ed5b6334204d8742919c3
+size 646

Models/LR_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5807fa23f76c4f7ce4b5a34e7fa58f01f1d1cd5275b43915e0e41faf21f0dfa
+size 452

Models/RF_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6a4ece78f8544a38a7167ee3970ede28067731e34cdfa19fc164673d74ccb1f
+size 757336

Models/best_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cba0ec727f1947f21c49b2c49fd76cdedab1549842adc599cec1b98d9893d43b
+size 267816

Models/target_scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5601fcc29222aa35f0b3980bacd32dcbac4368ad7399fdbf7b72710925a95ea1
+size 474

app.py

@@ -0,0 +1,221 @@
+# -*- coding: utf-8 -*-
+"""DashBoard.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/101_T8xMCWHcohzgIs8NvdK3wYK7h7lwO
+"""
+
+
+import gradio as gr
+import pandas as pd
+import pickle
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# تحميل النماذج من المجلد المحلي
+with open("Models/Feature_Scaler.pkl", "rb") as f:
+    scaler = pickle.load(f)
+
+with open("Models/target_scaler.pkl", "rb") as f:
+    target_scaler = pickle.load(f)
+
+with open("Models/LR_model.pkl", "rb") as f:
+    linear_model = pickle.load(f)
+
+with open("Models/DT_model.pkl", "rb") as f:
+    dt_model = pickle.load(f)
+
+with open("Models/RF_model.pkl", "rb") as f:
+    rf_model = pickle.load(f)
+
+lstm_model = load_model("Models/best_model.h5")
+
+# Create sequences for LSTM
+def create_sequences(data, window_size=11):
+    sequences = []
+    for i in range(len(data) - window_size + 1):
+        seq = data[i:i+window_size]
+        sequences.append(seq)
+    return np.array(sequences).astype('float32')
+
+# Data processing and alert function
+def process_and_alert(file):
+    try:
+        df = pd.read_csv(file.name)
+        df["timestamp"] = pd.to_datetime(df["timestamp"])
+        df = df.sort_values("timestamp").reset_index(drop=True)
+        df["fault_flag"] = df["status"].apply(lambda x: 1 if x == "fault" else 0)
+        fault_indices = df[df["fault_flag"] == 1].index.tolist()
+        time_to_failure = []
+        for i in range(len(df)):
+            next_faults = [j for j in fault_indices if j >= i]
+            if next_faults:
+                seconds = (df.loc[next_faults[0], "timestamp"] - df.loc[i, "timestamp"]).total_seconds()
+            else:
+                seconds = None
+            time_to_failure.append(seconds)
+        df["time_to_failure"] = time_to_failure
+        df.dropna(inplace=True)
+        X = df.drop(columns=['time_to_failure', 'fault_flag', 'status', 'timestamp'])
+        X_scaled = scaler.transform(X)
+
+        # LSTM processing
+        window_size = 11
+        if len(X_scaled) < window_size:
+            raise ValueError(f"Model requires at least {window_size} samples!")
+        X_seq = create_sequences(X_scaled, window_size)
+
+        # Predictions
+        pred_linear = linear_model.predict(X_scaled)
+        pred_dt = dt_model.predict(X_scaled)
+        pred_rf = rf_model.predict(X_scaled)
+        pred_lstm = lstm_model.predict(X_seq)
+
+        # Align lengths
+        min_length = min(len(pred_linear), len(pred_dt), len(pred_rf), len(pred_lstm))
+        pred_linear = pred_linear[:min_length]
+        pred_dt = pred_dt[:min_length]
+        pred_rf = pred_rf[:min_length]
+        pred_lstm = pred_lstm[:min_length]
+
+        # Inverse transform
+        pred_lstm = target_scaler.inverse_transform(pred_lstm.reshape(-1, 1))
+        pred_linear = target_scaler.inverse_transform(pred_linear.reshape(-1, 1))
+        pred_dt = target_scaler.inverse_transform(pred_dt.reshape(-1, 1))
+        pred_rf = target_scaler.inverse_transform(pred_rf.reshape(-1, 1))
+
+        def format_value(val):
+            return f'<span style="color:red;font-weight:bold;">{val:.2f} (Fault)</span>' if val < 0 else f'{val:.2f}'
+
+        html_rows = ""
+        for i in range(min_length):
+            html_rows += "<tr>" + "".join([
+                f"<td>{format_value(pred_linear[i][0])}-second</td>",
+                f"<td>{format_value(pred_dt[i][0])}-second</td>",
+                f"<td>{format_value(pred_rf[i][0])}-second</td>",
+                f"<td>{format_value(pred_lstm[i][0])}-second</td>"
+            ]) + "</tr>"
+
+        html_table = f"""
+        <table border="1" style="border-collapse:collapse; width:100%; text-align:center;">
+            <thead>
+                <tr style="background-color:#f0f0f0;">
+                    <th>Linear Regression</th>
+                    <th>Decision Tree</th>
+                    <th>Random Forest</th>
+                    <th>LSTM</th>
+                </tr>
+            </thead>
+            <tbody>
+                {html_rows}
+            </tbody>
+        </table>
+        """
+
+        # Alert System
+        preds = {
+            "Linear Regression": pred_linear,
+            "Decision Tree": pred_dt,
+            "Random Forest": pred_rf,
+            "LSTM": pred_lstm
+        }
+        alerts = []
+        for model_name, values in preds.items():
+            positives = values[values > 0]
+            if positives.size > 0:
+                min_pos = np.min(positives)
+                alerts.append((model_name, min_pos))
+        if not alerts:
+            alert_msg = "<h3 style='color:red;'>❌ No positive predictions found! Failure may have already occurred.</h3>"
+        else:
+            alerts.sort(key=lambda x: x[1])
+            best_model, time_left = alerts[0]
+            minutes = int(time_left // 60)
+            seconds = int(time_left % 60)
+            color = "red" if time_left < 60 else "orange" if time_left < 180 else "green"
+            msg = f"{minutes} minute(s) and {seconds} second(s)" if minutes > 0 else f"{seconds} second(s)"
+            alert_msg = f"""
+            <div style="padding:20px; border:2px solid {color}; border-radius:10px;">
+                <h3>🔔 Failure Alert</h3>
+                <p><strong>Model:</strong> <span style="color:blue;">{best_model}</span></p>
+                <p><strong>Estimated time to failure:</strong> <span style="color:{color}; font-weight:bold;">{msg}</span></p>
+                {"<p style='color:red; font-weight:bold;'>⚠️ Imminent failure!</p>" if time_left < 60 else ""}
+            </div>
+            """
+        return html_table, alert_msg
+    except Exception as e:
+        error_msg = f"<div style='color:red; padding:20px; border:2px solid red;'><h3>❌ Error:</h3><p>{str(e)}</p></div>"
+        return error_msg, ""
+
+# Load comparison tables
+def load_metrics():
+    return pd.read_csv("/content/drive/MyDrive/Analaysis for Time Fauiler/Models_Metrices.csv")
+
+def load_comparison():
+    return pd.read_csv("/content/drive/MyDrive/Analaysis for Time Fauiler/model_comparison_20250419_0955.csv")
+
+# Gradio UI
+with gr.Blocks(title="📊 Model Comparison Dashboard") as interface:
+    with gr.Tab("📈 Model Comparison"):
+        gr.Markdown("## 🔍 Actual vs Predicted")
+        with gr.Row():
+            gr.Image(value="/content/drive/MyDrive/Analaysis for Time Fauiler/Images/Liner_Regresion.png", label="Linear Regression")
+            gr.Image(value="/content/drive/MyDrive/Analaysis for Time Fauiler/Images/DT.png", label="Decision Tree")
+        with gr.Row():
+            gr.Image(value="/content/drive/MyDrive/Analaysis for Time Fauiler/Images/Random_Forest.png", label="Random Forest")
+            gr.Image(value="/content/drive/MyDrive/Analaysis for Time Fauiler/Images/LSTM.png", label="LSTM")
+        gr.Markdown("### 🧮 Data Distribution")
+        gr.Image(value="/content/drive/MyDrive/Analaysis for Time Fauiler/Images/Data_Dis.png", label="Data Distribution")
+        gr.Markdown("### 📋 Model Metrics Table")
+        gr.Dataframe(load_metrics, interactive=False)
+        gr.Markdown("### 🆕 Best and Worst Models Table")
+        gr.Dataframe(load_comparison, interactive=False)
+
+    with gr.Tab("📁 Upload Data"):
+        gr.Markdown("## 📥 Upload a new CSV file to analyze and detect failure")
+        file_input = gr.File(label="Choose CSV File")
+        output_html = gr.HTML(label="Prediction Results")
+        alert_output = gr.HTML(label="🔔 Alert")
+        file_input.change(fn=process_and_alert, inputs=file_input, outputs=[output_html, alert_output])
+
+    with gr.Tab("🧮 Manual Input"):
+        gr.Markdown("""
+            <div style="display:flex; align-items:center; gap:10px;">
+                <span style="font-size:30px;">🧾</span>
+                <h3 style="margin:0;">Enter 3 features to get a prediction and failure alert</h3>
+            </div>
+        """)
+        with gr.Row():
+            f1 = gr.Number(label="vibration")
+            f2 = gr.Number(label="temperature")
+            f3 = gr.Number(label="pressure")
+        result_output = gr.Textbox(label="🔍 Predicted Time (seconds)", interactive=False)
+        alert_output_ready = gr.HTML(label="🚨 Alert")
+
+        def predict_and_alert_ready(x1, x2, x3):
+            try:
+                X_input = np.array([[x1, x2, x3]])
+                X_scaled = scaler.transform(X_input)
+                pred = rf_model.predict(X_scaled).reshape(-1, 1)
+                pred_original = target_scaler.inverse_transform(pred)[0][0]
+                minutes = int(pred_original // 60)
+                seconds = int(pred_original % 60)
+                color = "red" if pred_original < 60 else "orange" if pred_original < 180 else "green"
+                msg = f"{minutes} minute(s) and {seconds} second(s)" if minutes > 0 else f"{seconds} second(s)"
+                alert_html = f"""
+                <div style="padding:15px; border:2px solid {color}; border-radius:10px;">
+                    <h3>📢 Advanced Alert</h3>
+                    <p><strong>Estimated time to failure:</strong> <span style="color:{color}; font-weight:bold;">{msg}</span></p>
+                    {"<p style='color:red; font-weight:bold;'>⚠️ Imminent failure!</p>" if pred_original < 60 else ""}
+                </div>
+                """
+                return f"{pred_original:.2f} seconds", alert_html
+            except Exception as e:
+                return "Input Error", f"<p style='color:red;'>❌ {str(e)}</p>"
+
+        btn = gr.Button("🔍 Predict Now")
+        btn.click(predict_and_alert_ready, inputs=[f1, f2, f3], outputs=[result_output, alert_output_ready])
+
+interface.launch(share=True)

cnc_sensor_data.csv

@@ -0,0 +1,1001 @@
+timestamp,vibration,temperature,pressure,status
+2025-04-01 08:00:00,0.23,67.0,3.29,normal
+2025-04-01 08:10:00,0.27,60.5,3.0,normal
+2025-04-01 08:20:00,0.29,60.8,3.13,normal
+2025-04-01 08:30:00,0.47,66.0,3.14,normal
+2025-04-01 08:40:00,0.98,82.8,4.99,fault
+2025-04-01 08:50:00,0.23,69.9,3.41,normal
+2025-04-01 09:00:00,0.32,67.0,3.1,normal
+2025-04-01 09:10:00,0.34,67.8,3.48,normal
+2025-04-01 09:20:00,0.38,67.6,2.97,normal
+2025-04-01 09:30:00,0.46,61.1,3.4,normal
+2025-04-01 09:40:00,0.33,66.4,3.33,normal
+2025-04-01 09:50:00,0.45,61.4,3.32,normal
+2025-04-01 10:00:00,0.95,80.2,4.67,fault
+2025-04-01 10:10:00,0.37,61.9,3.34,normal
+2025-04-01 10:20:00,0.3,60.6,3.05,normal
+2025-04-01 10:30:00,0.4,69.8,2.99,normal
+2025-04-01 10:40:00,0.93,77.2,4.79,fault
+2025-04-01 10:50:00,0.62,78.4,4.5,fault
+2025-04-01 11:00:00,0.69,82.1,4.91,fault
+2025-04-01 11:10:00,0.33,64.0,3.43,normal
+2025-04-01 11:20:00,0.35,67.8,3.42,normal
+2025-04-01 11:30:00,0.33,61.2,2.86,normal
+2025-04-01 11:40:00,0.45,67.5,2.81,normal
+2025-04-01 11:50:00,0.72,80.7,4.88,fault
+2025-04-01 12:00:00,0.8,81.4,4.97,fault
+2025-04-01 12:10:00,0.68,79.9,4.06,fault
+2025-04-01 12:20:00,0.32,64.5,3.13,normal
+2025-04-01 12:30:00,0.33,62.7,2.92,normal
+2025-04-01 12:40:00,0.45,68.1,3.36,normal
+2025-04-01 12:50:00,0.32,61.6,3.39,normal
+2025-04-01 13:00:00,0.93,78.3,4.36,fault
+2025-04-01 13:10:00,0.38,64.8,3.18,normal
+2025-04-01 13:20:00,0.38,65.5,3.01,normal
+2025-04-01 13:30:00,0.8,82.1,4.08,fault
+2025-04-01 13:40:00,0.3,64.9,3.31,normal
+2025-04-01 13:50:00,0.35,68.3,3.45,normal
+2025-04-01 14:00:00,0.25,64.6,3.1,normal
+2025-04-01 14:10:00,0.21,69.6,3.43,normal
+2025-04-01 14:20:00,0.77,84.8,4.59,fault
+2025-04-01 14:30:00,0.44,67.7,2.87,normal
+2025-04-01 14:40:00,0.49,64.6,2.87,normal
+2025-04-01 14:50:00,0.83,80.0,4.05,fault
+2025-04-01 15:00:00,0.49,67.8,2.82,normal
+2025-04-01 15:10:00,0.46,66.3,3.15,normal
+2025-04-01 15:20:00,0.89,80.6,4.71,fault
+2025-04-01 15:30:00,0.39,67.6,3.4,normal
+2025-04-01 15:40:00,0.99,80.2,4.23,fault
+2025-04-01 15:50:00,0.49,61.3,3.47,normal
+2025-04-01 16:00:00,0.88,75.1,4.25,fault
+2025-04-01 16:10:00,0.31,69.7,3.49,normal
+2025-04-01 16:20:00,0.47,61.0,2.98,normal
+2025-04-01 16:30:00,0.93,82.1,4.37,fault
+2025-04-01 16:40:00,0.29,69.0,3.23,normal
+2025-04-01 16:50:00,0.36,60.0,3.23,normal
+2025-04-01 17:00:00,0.49,68.5,3.0,normal
+2025-04-01 17:10:00,0.41,68.6,3.31,normal
+2025-04-01 17:20:00,0.21,65.1,3.12,normal
+2025-04-01 17:30:00,0.22,64.4,2.91,normal
+2025-04-01 17:40:00,0.38,60.5,3.03,normal
+2025-04-01 17:50:00,0.37,61.3,3.36,normal
+2025-04-01 18:00:00,0.86,81.6,4.64,fault
+2025-04-01 18:10:00,0.93,76.5,4.12,fault
+2025-04-01 18:20:00,0.96,78.1,4.95,fault
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model_comparison_20250420_0853.csv

@@ -0,0 +1,5 @@
+النموذج,التنبؤ الأفضل,الخطأ في الأفضل,التنبؤ الأسوأ,الخطأ في الأسوأ,وقت الأفضل,وقت الأسوأ
+Random Forest,-0.00,0.00,3267.32,10532.68,2025-04-01 08:50:00,2025-04-01 21:20:00
+LSTM,2978.68,21.32,3196.60,10603.40,2025-04-01 22:20:00,2025-04-01 19:10:00
+Linear Regression,6.43,6.43,3525.86,10274.14,2025-04-01 19:40:00,2025-04-01 21:20:00
+Decision Tree Regressor,-0.00,0.00,2344.19,11455.81,2025-04-01 08:50:00,2025-04-01 21:20:00

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.0
+pandas
+numpy
+tensorflow
+scikit-learn
+pickle-mixin