177 lines
10 KiB
Plaintext
177 lines
10 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2f54e447-af06-4046-85b9-b340b8c855b3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" * Serving Flask app '__main__'\n",
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" * Debug mode: off\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
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" * Running on http://127.0.0.1:5001\n",
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"\u001b[33mPress CTRL+C to quit\u001b[0m\n",
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"127.0.0.1 - - [19/Feb/2024 19:27:12] \"POST /pytanie HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [19/Feb/2024 19:27:13] \"POST /pytanie HTTP/1.1\" 200 -\n",
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"127.0.0.1 - - [19/Feb/2024 19:27:13] \"POST /pytanie HTTP/1.1\" 200 -\n"
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]
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}
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],
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"source": [
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"from flask import Flask, request, jsonify\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import joblib\n",
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"\n",
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"df = pd.read_csv('Lista_projektow_FE_2014_2020_020124.csv')\n",
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"tfidf_vectorizer=joblib.load(\"vectoriser.sav\")\n",
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"model_fin_warto=joblib.load(\"model_fin_warto.sav\")\n",
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"model_fin_kwali=joblib.load(\"model_fin_kwali.sav\")\n",
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"model_fin_dofin=joblib.load(\"model_fin_dofin.sav\")\n",
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"model_fundusz=joblib.load(\"model_fundusz.sav\")\n",
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"model_dziedz=joblib.load(\"model_dziedz.sav\")\n",
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"nn_model =joblib.load(\"model_nn.sav\")\n",
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"\n",
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"app = Flask(__name__)\n",
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"\n",
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"@app.route('/pytanie', methods=['POST'])\n",
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"def obsluga_pytania():\n",
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" data = request.get_json()\n",
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" pytanie = data.get('pytanie', '')\n",
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"\n",
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" if pytanie:\n",
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" pytanie_tfidf = tfidf_vectorizer.transform([pytanie])\n",
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" projects = nn_model.kneighbors(pytanie_tfidf)[1].flatten()\n",
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" return jsonify(\n",
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" Predicted_fin_warto = str(np.round(np.exp(model_fin_warto.predict(pytanie_tfidf)),2)),\n",
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" Predicted_fin_kwali = str(np.round(np.exp(model_fin_kwali.predict(pytanie_tfidf)),2)),\n",
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" Predicted_fin_dofin = str(np.round(np.exp(model_fin_dofin.predict(pytanie_tfidf)),2)),\n",
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" Predicted_fundusz = str(model_fundusz.predict(pytanie_tfidf)),\n",
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" Predicted_dziedzina = str(model_dziedz.predict(pytanie_tfidf)),\n",
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" P1_Project_name = str(df.iloc[projects[0]].iloc[0]),\n",
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" P1_Project_summary = str(df.iloc[projects[0]].iloc[1]),\n",
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" P1_Contract_number = str(df.iloc[projects[0]].iloc[2]),\n",
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" P1_Beneficiary_name = str(df.iloc[projects[0]].iloc[3]),\n",
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" P1_Fund = str(df.iloc[projects[0]].iloc[4]),\n",
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" P1_Programme = str(df.iloc[projects[0]].iloc[5]),\n",
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" P1_Priority_axis = str(df.iloc[projects[0]].iloc[6]),\n",
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" P1_Measure = str(df.iloc[projects[0]].iloc[7]),\n",
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" P1_Submeasure = str(df.iloc[projects[0]].iloc[8]),\n",
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" P1_Total_project_value = str(df.iloc[projects[0]].iloc[9]),\n",
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" P1_Total_eligible_expenditure = str(df.iloc[projects[0]].iloc[10]),\n",
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" P1_Amount_of_EU_co_financing = str(df.iloc[projects[0]].iloc[11]),\n",
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" P1_Union_co_financing_rate = str(df.iloc[projects[0]].iloc[12]),\n",
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" P1_Form_of_finance = str(df.iloc[projects[0]].iloc[13]),\n",
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" P1_Project_location = str(df.iloc[projects[0]].iloc[14]),\n",
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" P1_Territory_type = str(df.iloc[projects[0]].iloc[15]),\n",
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" P1_Project_start_date = str(df.iloc[projects[0]].iloc[16]),\n",
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" P1_Project_end_date = str(df.iloc[projects[0]].iloc[17]),\n",
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" P1_Project_implemented_under_competitive_or_non_competitive_procedure = str(df.iloc[projects[0]].iloc[18]),\n",
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" P1_Area_of_economic_activity = str(df.iloc[projects[0]].iloc[19]),\n",
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" P1_Area_of_project_intervention = str(df.iloc[projects[0]].iloc[20]),\n",
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" P1_Project_thematic_objective = str(df.iloc[projects[0]].iloc[21]),\n",
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" P1_ESF_secondary_theme = str(df.iloc[projects[0]].iloc[22]),\n",
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" P1_Project_implemented_under_territorial_delivery_mechanisms = str(df.iloc[projects[0]].iloc[23]),\n",
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" P1_Funding_completed = str(df.iloc[projects[0]].iloc[24]),\n",
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" P2_Project_name = str(df.iloc[projects[1]].iloc[0]),\n",
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" P2_Project_summary = str(df.iloc[projects[1]].iloc[1]),\n",
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" P2_Contract_number = str(df.iloc[projects[1]].iloc[2]),\n",
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" P2_Beneficiary_name = str(df.iloc[projects[1]].iloc[3]),\n",
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" P2_Fund = str(df.iloc[projects[1]].iloc[4]),\n",
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" P2_Programme = str(df.iloc[projects[1]].iloc[5]),\n",
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" P2_Priority_axis = str(df.iloc[projects[1]].iloc[6]),\n",
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" P2_Measure = str(df.iloc[projects[1]].iloc[7]),\n",
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" P2_Submeasure = str(df.iloc[projects[1]].iloc[8]),\n",
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" P2_Total_project_value = str(df.iloc[projects[1]].iloc[9]),\n",
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" P2_Total_eligible_expenditure = str(df.iloc[projects[1]].iloc[10]),\n",
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" P2_Amount_of_EU_co_financing = str(df.iloc[projects[1]].iloc[11]),\n",
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" P2_Union_co_financing_rate = str(df.iloc[projects[1]].iloc[12]),\n",
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" P2_Form_of_finance = str(df.iloc[projects[1]].iloc[13]),\n",
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" P2_Project_location = str(df.iloc[projects[1]].iloc[14]),\n",
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" P2_Territory_type = str(df.iloc[projects[1]].iloc[15]),\n",
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" P2_Project_start_date = str(df.iloc[projects[1]].iloc[16]),\n",
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" P2_Project_end_date = str(df.iloc[projects[1]].iloc[17]),\n",
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" P2_Project_implemented_under_competitive_or_non_competitive_procedure = str(df.iloc[projects[1]].iloc[18]),\n",
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" P2_Area_of_economic_activity = str(df.iloc[projects[1]].iloc[19]),\n",
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" P2_Area_of_project_intervention = str(df.iloc[projects[1]].iloc[20]),\n",
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" P2_Project_thematic_objective = str(df.iloc[projects[1]].iloc[21]),\n",
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" P2_ESF_secondary_theme = str(df.iloc[projects[1]].iloc[22]),\n",
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" P2_Project_implemented_under_territorial_delivery_mechanisms = str(df.iloc[projects[1]].iloc[23]),\n",
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" P2_Funding_completed = str(df.iloc[projects[1]].iloc[24]),\n",
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" P3_Project_name = str(df.iloc[projects[2]].iloc[0]),\n",
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" P3_Project_summary = str(df.iloc[projects[2]].iloc[1]),\n",
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" P3_Contract_number = str(df.iloc[projects[2]].iloc[2]),\n",
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" P3_Beneficiary_name = str(df.iloc[projects[2]].iloc[3]),\n",
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" P3_Fund = str(df.iloc[projects[2]].iloc[4]),\n",
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" P3_Programme = str(df.iloc[projects[2]].iloc[5]),\n",
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" P3_Priority_axis = str(df.iloc[projects[2]].iloc[6]),\n",
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" P3_Measure = str(df.iloc[projects[2]].iloc[7]),\n",
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" P3_Submeasure = str(df.iloc[projects[2]].iloc[8]),\n",
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" P3_Total_project_value = str(df.iloc[projects[2]].iloc[9]),\n",
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" P3_Total_eligible_expenditure = str(df.iloc[projects[2]].iloc[10]),\n",
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" P3_Amount_of_EU_co_financing = str(df.iloc[projects[2]].iloc[11]),\n",
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" P3_Union_co_financing_rate = str(df.iloc[projects[2]].iloc[12]),\n",
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" P3_Form_of_finance = str(df.iloc[projects[2]].iloc[13]),\n",
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" P3_Project_location = str(df.iloc[projects[2]].iloc[14]),\n",
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" P3_Territory_type = str(df.iloc[projects[2]].iloc[15]),\n",
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" P3_Project_start_date = str(df.iloc[projects[2]].iloc[16]),\n",
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" P3_Project_end_date = str(df.iloc[projects[2]].iloc[17]),\n",
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" P3_Project_implemented_under_competitive_or_non_competitive_procedure = str(df.iloc[projects[2]].iloc[18]),\n",
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" P3_Area_of_economic_activity = str(df.iloc[projects[2]].iloc[19]),\n",
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" P3_Area_of_project_intervention = str(df.iloc[projects[2]].iloc[20]),\n",
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" P3_Project_thematic_objective = str(df.iloc[projects[2]].iloc[21]),\n",
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" P3_ESF_secondary_theme = str(df.iloc[projects[2]].iloc[22]),\n",
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" P3_Project_implemented_under_territorial_delivery_mechanisms = str(df.iloc[projects[2]].iloc[23]),\n",
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" P3_Funding_completed = str(df.iloc[projects[2]].iloc[24])\n",
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" )\n",
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" else:\n",
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" return jsonify({'error': 'Brak pytania'}), 400\n",
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"\n",
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"if __name__ == '__main__':\n",
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" app.run(debug=False, port=5001)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1e33ccee-21cd-4e47-bf26-d324d777fc81",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.info()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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