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Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "806d60dd-cc27-4d23-add1-7cd28bbaa0fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\\utils.py:953: UserWarning: Expected 1 arguments for function <function predict_user_profile at 0x000001DFE61E2840>, received 7.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\\utils.py:961: UserWarning: Expected maximum 1 arguments for function <function predict_user_profile at 0x000001DFE61E2840>, received 7.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\\queueing.py\", line 527, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\\route_utils.py\", line 270, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 1847, in process_api\n",
+      "    result = await self.call_function(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\\blocks.py\", line 1433, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "           ^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 851, in run\n",
+      "    result = context.run(func, *args)\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\\utils.py\", line 788, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "               ^^^^^^^^^^^^^^^^^^\n",
+      "TypeError: predict_user_profile() takes 1 positional argument but 7 were given\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Could not create share link. Missing file: C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\\frpc_windows_amd64_v0.2. \n",
+      "\n",
+      "Please check your internet connection. This can happen if your antivirus software blocks the download of this file. You can install manually by following these steps: \n",
+      "\n",
+      "1. Download this file: https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_windows_amd64.exe\n",
+      "2. Rename the downloaded file to: frpc_windows_amd64_v0.2\n",
+      "3. Move the file to this location: C:\\Users\\mistr\\anaconda3\\Lib\\site-packages\\gradio\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "# Load the trained model from data.pkl\n",
+    "def load_model():\n",
+    "  with open('data.pkl', 'rb') as file:\n",
+    "    model = pickle.load(file)\n",
+    "  return model\n",
+    "\n",
+    "# Define the prediction function using the loaded model\n",
+    "def predict_user_profile(inputs):\n",
+    "  # Preprocess the input data\n",
+    "  lang_encoder = LabelEncoder()\n",
+    "  lang_code = lang_encoder.fit_transform([inputs['Language']])[0]\n",
+    "\n",
+    "  # Create a DataFrame from the user input dictionary\n",
+    "  df = pd.DataFrame.from_dict([inputs])\n",
+    "\n",
+    "  # Select the relevant feature columns used during model training\n",
+    "  feature_columns_to_use = ['statuses_count', 'followers_count', 'friends_count',\n",
+    "                           'favourites_count', 'listed_count', 'lang_code']\n",
+    "  df_features = df[feature_columns_to_use]\n",
+    "\n",
+    "  # Load the pre-trained model\n",
+    "  model = load_model()\n",
+    "\n",
+    "  # Make predictions using the loaded model\n",
+    "  prediction = model.predict(df_features)\n",
+    "\n",
+    "  # Return the predicted class label (0 for fake, 1 for genuine)\n",
+    "  return \"Genuine\" if prediction[0] == 1 else \"Fake\"\n",
+    "\n",
+    "# Define the Gradio interface\n",
+    "inputs = [\n",
+    "  gr.Textbox(label=\"statuses_count\"),\n",
+    "  gr.Textbox(label=\"followers_count\"),\n",
+    "  gr.Textbox(label=\"friends_count\"),\n",
+    "  gr.Textbox(label=\"favourites_count\"),\n",
+    "  gr.Textbox(label=\"listed_count\"),\n",
+    "  gr.Textbox(label=\"name\"),\n",
+    "  gr.Textbox(label=\"Language\"),\n",
+    "]\n",
+    "\n",
+    "outputs = gr.Textbox(label=\"Prediction\")\n",
+    "\n",
+    "# Create the Gradio interface\n",
+    "interface = gr.Interface(fn=predict_user_profile, inputs=inputs, outputs=outputs,\n",
+    "                         title='User Profile Classifier',\n",
+    "                         description='Predict whether a user profile is genuine or fake.')\n",
+    "\n",
+    "interface.launch(share=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02a483bc-0d49-45e5-908e-eab4769ac7af",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1443145-496e-4cc4-a516-0ea70e4cc1fb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58478763-b66d-4841-a965-089b1681b3c0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a7e7ed00f4b01521ea4119232c9f303cc42c77430fc2b4481340d24567a4f282
+size 181103