Max005/DeepfakeDetection

1{2 "cells": [3  {4   "cell_type": "code",5   "execution_count": 4,6   "metadata": {},7   "outputs": [],8   "source": [9    "import requests\n",10    "\n",11    "def infer_text(api_url, input_text):\n",12    "    url = f\"{api_url}/infer\"\n",13    "    try:\n",14    "        # Send the input as a JSON object\n",15    "        response = requests.post(url, json={\"input\": input_text})\n",16    "        response.raise_for_status()\n",17    "        return response.json()\n",18    "    except requests.exceptions.RequestException as e:\n",19    "        print(f\"Error during API call: {e}\")\n",20    "        return None\n",21    "\n",22    "def check_health(api_url):\n",23    "    url = f\"{api_url}/health\"\n",24    "    try:\n",25    "        response = requests.get(url)\n",26    "        response.raise_for_status()\n",27    "        return response.json()\n",28    "    except requests.exceptions.RequestException as e:\n",29    "        print(f\"Error during API health check: {e}\")\n",30    "        return None"31   ]32  },33  {34   "cell_type": "code",35   "execution_count": 5,36   "metadata": {},37   "outputs": [38    {39     "name": "stdout",40     "output_type": "stream",41     "text": [42      "API Health Check: {'message': 'ok'}\n",43      "Predictions: [{'label': 'LABEL_0', 'score': 0.9927427768707275}]\n"44     ]45    }46   ],47   "source": [48    "api_url = \"http://localhost:8000\"\n",49    "\n",50    "# Check the API health status\n",51    "health_status = check_health(api_url)\n",52    "if health_status:\n",53    "    print(\"API Health Check:\", health_status)\n",54    "else:\n",55    "    print(\"Failed to connect to the API.\")\n",56    "\n",57    "# Example input text\n",58    "input_text = \"Congratulations! You've won a prize. Click the link to claim your reward.\"\n",59    "\n",60    "# Call the /infer endpoint\n",61    "predictions = infer_text(api_url, input_text)\n",62    "if predictions:\n",63    "    print(\"Predictions:\", predictions)\n",64    "else:\n",65    "    print(\"Failed to get predictions from the API.\")"66   ]67  },68  {69   "cell_type": "markdown",70   "metadata": {},71   "source": [72    "DeepFakeModel Test"73   ]74  },75  {76   "cell_type": "code",77   "execution_count": 4,78   "metadata": {},79   "outputs": [80    {81     "name": "stdout",82     "output_type": "stream",83     "text": [84      "Response JSON: {'predicted_label': 'Real', 'average_confidence': 0.9984144032001495}\n"85     ]86    }87   ],88   "source": [89    "import requests\n",90    "\n",91    "# Define the API endpoint\n",92    "url = \"http://127.0.0.1:8000/infer\"\n",93    "\n",94    "# Path to the audio file you want to test\n",95    "file_path = r\"D:\\repos\\GODAM\\audioFiles\\test.wav\"  # Replace with the path to your audio file\n",96    "\n",97    "# Open the file in binary mode\n",98    "with open(file_path, \"rb\") as audio_file:\n",99    "    # Prepare the file payload\n",100    "    files = {\"file\": (\"audio.wav\", audio_file, \"audio/wav\")}\n",101    "    \n",102    "    # Send the POST request\n",103    "    response = requests.post(url, files=files)\n",104    "\n",105    "# Print the response from the API\n",106    "if response.status_code == 200:\n",107    "    print(\"Response JSON:\", response.json())\n",108    "else:\n",109    "    print(f\"Error {response.status_code}: {response.text}\")"110   ]111  }112 ],113 "metadata": {114  "kernelspec": {115   "display_name": "base",116   "language": "python",117   "name": "python3"118  },119  "language_info": {120   "codemirror_mode": {121    "name": "ipython",122    "version": 3123   },124   "file_extension": ".py",125   "mimetype": "text/x-python",126   "name": "python",127   "nbconvert_exporter": "python",128   "pygments_lexer": "ipython3",129   "version": "3.12.7"130  }131 },132 "nbformat": 4,133 "nbformat_minor": 2134}135