{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "def infer_text(api_url, input_text):\n",
    "    url = f\"{api_url}/infer\"\n",
    "    try:\n",
    "        # Send the input as a JSON object\n",
    "        response = requests.post(url, json={\"input\": input_text})\n",
    "        response.raise_for_status()\n",
    "        return response.json()\n",
    "    except requests.exceptions.RequestException as e:\n",
    "        print(f\"Error during API call: {e}\")\n",
    "        return None\n",
    "\n",
    "def check_health(api_url):\n",
    "    url = f\"{api_url}/health\"\n",
    "    try:\n",
    "        response = requests.get(url)\n",
    "        response.raise_for_status()\n",
    "        return response.json()\n",
    "    except requests.exceptions.RequestException as e:\n",
    "        print(f\"Error during API health check: {e}\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API Health Check: {'message': 'ok'}\n",
      "Predictions: [{'label': 'LABEL_0', 'score': 0.9927427768707275}]\n"
     ]
    }
   ],
   "source": [
    "api_url = \"http://localhost:8000\"\n",
    "\n",
    "# Check the API health status\n",
    "health_status = check_health(api_url)\n",
    "if health_status:\n",
    "    print(\"API Health Check:\", health_status)\n",
    "else:\n",
    "    print(\"Failed to connect to the API.\")\n",
    "\n",
    "# Example input text\n",
    "input_text = \"Congratulations! You've won a prize. Click the link to claim your reward.\"\n",
    "\n",
    "# Call the /infer endpoint\n",
    "predictions = infer_text(api_url, input_text)\n",
    "if predictions:\n",
    "    print(\"Predictions:\", predictions)\n",
    "else:\n",
    "    print(\"Failed to get predictions from the API.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DeepFakeModel Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response JSON: {'predicted_label': 'Real', 'average_confidence': 0.9984144032001495}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "# Define the API endpoint\n",
    "url = \"http://127.0.0.1:8000/infer\"\n",
    "\n",
    "# Path to the audio file you want to test\n",
    "file_path = r\"D:\\repos\\GODAM\\audioFiles\\test.wav\"  # Replace with the path to your audio file\n",
    "\n",
    "# Open the file in binary mode\n",
    "with open(file_path, \"rb\") as audio_file:\n",
    "    # Prepare the file payload\n",
    "    files = {\"file\": (\"audio.wav\", audio_file, \"audio/wav\")}\n",
    "    \n",
    "    # Send the POST request\n",
    "    response = requests.post(url, files=files)\n",
    "\n",
    "# Print the response from the API\n",
    "if response.status_code == 200:\n",
    "    print(\"Response JSON:\", response.json())\n",
    "else:\n",
    "    print(f\"Error {response.status_code}: {response.text}\")"
   ]
  }
 ],
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