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{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-01-11T19:07:39.073318726Z",
"start_time": "2025-01-11T19:07:38.201074211Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction Result: {'prediction': 'healthy'}\n"
]
}
],
"source": [
"import requests\n",
"\n",
"# Define the URL of the FastAPI endpoint\n",
"url = \"http://127.0.0.1:8000/health_predict\" # Replace with the actual endpoint if hosted remotely\n",
"\n",
"# Define the input payload\n",
"payload = {\n",
" \"Gender\": \"M\",\n",
" \"Age\": 67,\n",
" \"SBP\": 145,\n",
" \"HBP\": 84,\n",
" \"heart_rate\": 116,\n",
" \"Glucose\": 128,\n",
" \"SpO2\": 98,\n",
" \"Temprature\": 97.8\n",
"}\n",
"\n",
"# Make the POST request\n",
"response = requests.post(url, json=payload)\n",
"\n",
"# Print the response\n",
"if response.status_code == 200:\n",
" print(\"Prediction Result:\", response.json())\n",
"else:\n",
" print(f\"Error: {response.status_code}, Message: {response.text}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction: Not Fraud\n"
]
}
],
"source": [
"import requests\n",
"\n",
"# URL of the FastAPI endpoint\n",
"url = \"http://127.0.0.1:8000/fraud_predict\"\n",
"\n",
"# Sample data to send in the POST request (make sure the data format matches the model)\n",
"input_data = {\n",
" \"V1\": 0.1,\n",
" \"V2\": 0.4,\n",
" \"V3\": 0.7,\n",
" \"V4\": 1.0,\n",
" \"V5\": 1.3,\n",
" \"V6\": 0.1,\n",
" \"V7\": 0.4,\n",
" \"V8\": 0.7,\n",
" \"V9\": 1.0,\n",
" \"V10\": 1.3,\n",
" \"V11\": 0.1,\n",
" \"V12\": 0.4,\n",
" \"V13\": 0.7,\n",
" \"V14\": 1.0,\n",
" \"V15\": 1.3,\n",
" \"V16\": 0.1,\n",
" \"V17\": 0.4,\n",
" \"V18\": 0.7,\n",
" \"V19\": 1.0,\n",
" \"V20\": 1.3,\n",
" \"V21\": 0.1,\n",
" \"V22\": 0.4,\n",
" \"V23\": 0.7,\n",
" \"V24\": 1.0,\n",
" \"V25\": 1.3,\n",
" \"V26\": 0.1,\n",
" \"V27\": 0.4,\n",
" \"V28\": 0.7,\n",
" \"Amount\": 100\n",
"}\n",
"\n",
"# Send the POST request to the FastAPI server\n",
"response = requests.post(url, json=input_data)\n",
"\n",
"# Check if the request was successful and print the response\n",
"if response.status_code == 200:\n",
" result = response.json()\n",
" print(\"Prediction:\", result[\"prediction\"])\n",
"else:\n",
" print(\"Error:\", response.status_code, response.text)\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2025-01-11T19:11:14.154786492Z",
"start_time": "2025-01-11T19:11:13.225551826Z"
}
},
"id": "39edb6c6f953f8df"
},
{
"cell_type": "code",
"execution_count": 17,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction Result: {'prediction': 'not arrest'}\n"
]
}
],
"source": [
"import requests\n",
"\n",
"# Sample data to send in the request\n",
"sample_data = {\n",
" \"Case\": \"JF113025\",\n",
" \"Block\": \"067XX S MORGAN ST\",\n",
" \"IUCR\": 2826,\n",
" \"Primary_Type\": \"OTHER OFFENSE\",\n",
" \"Description\": \"HARASSMENT BY ELECTRONIC MEANS\",\n",
" \"Location_Description\": \"RESIDENCE\",\n",
" \"FBI_Code\": 26,\n",
" \"Updated_On\": \"9/14/2023 15:41\",\n",
" \"Location\": \"(41.771782439, -87.649436929)\"\n",
"}\n",
"\n",
"# URL for FastAPI endpoint\n",
"url = \"http://127.0.0.1:8000/predict_crime\"\n",
"\n",
"# Send a POST request with the sample data as JSON\n",
"response = requests.post(url, json=sample_data)\n",
"\n",
"# Check if the request was successful\n",
"if response.status_code == 200:\n",
" print(f\"Prediction Result: {response.json()}\")\n",
"else:\n",
" print(f\"Error: {response.status_code}, {response.text}\")\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2025-01-11T19:44:26.136356206Z",
"start_time": "2025-01-11T19:44:25.549072705Z"
}
},
"id": "be329568072d336c"
},
{
"cell_type": "code",
"execution_count": 18,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-01-12 00:45:43.425294: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2025-01-12 00:45:44.479984: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"fastapi version: 0.115.4\n",
"pydantic version: 2.9.2\n",
"pickle version: 4.0\n",
"joblib version: 1.3.2\n",
"numpy version: 1.26.4\n",
"tensorflow version: 2.16.1\n",
"pandas version: 2.2.0\n"
]
}
],
"source": [
"import fastapi\n",
"import pydantic\n",
"import pickle\n",
"import joblib\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import pandas as pd\n",
"\n",
"# Print the versions of each library\n",
"print(f\"fastapi version: {fastapi.__version__}\")\n",
"print(f\"pydantic version: {pydantic.__version__}\")\n",
"print(f\"pickle version: {pickle.format_version}\") # pickle doesn't have __version__, but you can check the format version\n",
"print(f\"joblib version: {joblib.__version__}\")\n",
"print(f\"numpy version: {np.__version__}\")\n",
"print(f\"tensorflow version: {tf.__version__}\")\n",
"print(f\"pandas version: {pd.__version__}\")\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2025-01-11T19:45:45.753678471Z",
"start_time": "2025-01-11T19:45:42.265117643Z"
}
},
"id": "c76b855ced5fe0a3"
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
},
"id": "fc1962a8e8381309"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
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"nbformat_minor": 5
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