191 lines
7.2 KiB
Python
191 lines
7.2 KiB
Python
from fastapi import FastAPI, File, UploadFile, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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import pandas as pd
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import io
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from pydantic import BaseModel
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from typing import Dict, List
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app = FastAPI()
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# Configure CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # Allows all origins
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allow_credentials=True,
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allow_methods=["*"], # Allows all methods
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allow_headers=["*"], # Allows all headers
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)
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# --- In-memory Storage & Data Loading ---
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df_storage = None
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plz_column_name = None
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plz_geocoord_df = None
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@app.on_event("startup")
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def load_plz_data():
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global plz_geocoord_df
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try:
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print("--- Loading PLZ geocoordinates dataset... ---")
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# The CSV has a malformed header. We read it and assign names manually.
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df = pd.read_csv("plz_geocoord.csv", dtype=str)
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# Rename the columns based on their expected order: PLZ, Latitude, Longitude
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df.columns = ['plz', 'y', 'x']
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df['plz'] = df['plz'].str.zfill(5)
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plz_geocoord_df = df.set_index('plz')
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print(f"--- Successfully loaded {len(plz_geocoord_df)} PLZ coordinates. ---")
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except FileNotFoundError:
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print("--- FATAL ERROR: plz_geocoord.csv not found. Geocoding will not work. ---")
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plz_geocoord_df = pd.DataFrame()
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except Exception as e:
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print(f"--- FATAL ERROR loading plz_geocoord.csv: {e} ---")
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plz_geocoord_df = pd.DataFrame()
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# --- Pydantic Models ---
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class FilterRequest(BaseModel):
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filters: Dict[str, List[str]]
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# --- API Endpoints ---
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@app.get("/")
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def read_root():
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return {"message": "Heatmap Tool Backend"}
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@app.post("/api/upload")
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async def upload_file(file: UploadFile = File(...)):
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global df_storage, plz_column_name
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print(f"--- Received request to /api/upload for file: {file.filename} ---")
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if not file.filename.endswith('.xlsx'):
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raise HTTPException(status_code=400, detail="Invalid file format. Please upload an .xlsx file.")
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try:
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contents = await file.read()
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df = pd.read_excel(io.BytesIO(contents), dtype=str) # Read all as string to be safe
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df.fillna('N/A', inplace=True)
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# --- PLZ Column Detection ---
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temp_plz_col = None
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for col in df.columns:
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if 'plz' in col.lower():
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temp_plz_col = col
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break
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if not temp_plz_col:
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raise HTTPException(status_code=400, detail="No column with 'PLZ' found in the file.")
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plz_column_name = temp_plz_col
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# Normalize PLZ data
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df[plz_column_name] = df[plz_column_name].str.strip().str.zfill(5)
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# --- Dynamic Filter Detection ---
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filters = {}
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for col in df.columns:
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if col != plz_column_name:
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unique_values = df[col].unique().tolist()
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filters[col] = sorted(unique_values)
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df_storage = df
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print(f"Successfully processed file. Found PLZ column: '{plz_column_name}'. Detected {len(filters)} filterable columns.")
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return {"filename": file.filename, "filters": filters, "plz_column": plz_column_name}
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except Exception as e:
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print(f"ERROR processing file: {e}")
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raise HTTPException(status_code=500, detail=f"An error occurred while processing the file: {e}")
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@app.post("/api/heatmap")
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async def get_heatmap_data(request: FilterRequest):
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global df_storage, plz_column_name, plz_geocoord_df
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print(f"--- Received request to /api/heatmap with filters: {request.filters} ---")
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if df_storage is None:
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print("ERROR: No data in df_storage. File must be uploaded first.")
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raise HTTPException(status_code=404, detail="No data available. Please upload a file first.")
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if plz_geocoord_df.empty:
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raise HTTPException(status_code=500, detail="Geocoding data is not available on the server.")
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try:
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filtered_df = df_storage.copy()
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# Apply filters from the request
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for column, values in request.filters.items():
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if values:
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filtered_df = filtered_df[filtered_df[column].isin(values)]
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if filtered_df.empty:
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return []
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# Aggregate data by PLZ, and also collect attribute summaries
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plz_grouped = filtered_df.groupby(plz_column_name)
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plz_counts = plz_grouped.size().reset_index(name='count')
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# Collect unique attributes for each PLZ
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attribute_summaries = {}
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for plz_val, group in plz_grouped:
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summary = {}
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for col in filtered_df.columns:
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if col != plz_column_name and col != 'lat' and col != 'lon': # Exclude lat/lon if they somehow exist
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unique_attrs = group[col].unique().tolist()
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# Limit to top 3 unique values for readability
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summary[col] = unique_attrs[:3]
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attribute_summaries[plz_val] = summary
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# Convert summaries to a DataFrame for merging
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summary_df = pd.DataFrame.from_dict(attribute_summaries, orient='index')
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summary_df.index.name = plz_column_name
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# --- Geocoding Step ---
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# Merge the aggregated counts with the geocoding dataframe
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merged_df = pd.merge(
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plz_counts,
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plz_geocoord_df,
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left_on=plz_column_name,
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right_index=True,
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how='inner'
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)
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# Merge with attribute summaries
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merged_df = pd.merge(
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merged_df,
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summary_df,
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left_on=plz_column_name,
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right_index=True,
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how='left'
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)
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# Rename columns to match frontend expectations ('lon' and 'lat')
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merged_df.rename(columns={'x': 'lon', 'y': 'lat'}, inplace=True)
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# Also rename the original PLZ column to the consistent name 'plz'
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merged_df.rename(columns={plz_column_name: 'plz'}, inplace=True)
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# Convert to the required JSON format, including all remaining columns (which are the attributes)
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# We'll dynamically collect attribute columns for output
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output_columns = ['plz', 'lat', 'lon', 'count']
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for col in merged_df.columns:
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if col not in output_columns and col != plz_column_name: # Ensure we don't duplicate PLZ or coords
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output_columns.append(col)
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heatmap_data = merged_df[output_columns].to_dict(orient='records')
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# The frontend expects 'attributes_summary' as a single field, so let's restructure for that
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# For each record, pick out the attributes that are not 'plz', 'lat', 'lon', 'count'
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final_heatmap_data = []
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for record in heatmap_data:
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attrs = {k: v for k, v in record.items() if k not in ['plz', 'lat', 'lon', 'count']}
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final_heatmap_data.append({
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"plz": record['plz'],
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"lat": record['lat'],
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"lon": record['lon'],
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"count": record['count'],
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"attributes_summary": attrs
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})
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print(f"Generated heatmap data with {len(final_heatmap_data)} PLZ points.")
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return final_heatmap_data
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except Exception as e:
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print(f"ERROR generating heatmap: {e}")
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raise HTTPException(status_code=500, detail=f"An error occurred while generating heatmap data: {e}")
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