feat([2fd88f42]): initial setup of heatmap tool

heatmap-tool/backend/main.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import pandas as pd
+import io
+from pydantic import BaseModel
+from typing import Dict, List
+
+app = FastAPI()
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allows all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allows all methods
+    allow_headers=["*"],  # Allows all headers
+)
+
+# --- In-memory Storage ---
+df_storage = None
+plz_column_name = None
+
+# --- Dummy Geocoding Data (IMPORTANT: TO BE REPLACED) ---
+# This is a tiny subset of German postal codes for demonstration purposes only.
+# A real implementation MUST load a comprehensive PLZ dataset from a file 
+# (e.g., a CSV or GeoJSON file) for the application to be useful.
+PLZ_COORDINATES = {
+    "10115": {"lat": 52.53, "lon": 13.38}, # Berlin
+    "20095": {"lat": 53.55, "lon": 9.99},  # Hamburg
+    "80331": {"lat": 48.13, "lon": 11.57}, # Munich
+    "50667": {"lat": 50.93, "lon": 6.95},  # Cologne
+    "60311": {"lat": 50.11, "lon": 8.68},  # Frankfurt
+}
+
+# --- Pydantic Models ---
+class FilterRequest(BaseModel):
+    filters: Dict[str, List[str]]
+
+# --- API Endpoints ---
+@app.get("/")
+def read_root():
+    return {"message": "Heatmap Tool Backend"}
+
+@app.post("/api/upload")
+async def upload_file(file: UploadFile = File(...)):
+    global df_storage, plz_column_name
+    if not file.filename.endswith('.xlsx'):
+        raise HTTPException(status_code=400, detail="Invalid file format. Please upload an .xlsx file.")
+
+    try:
+        contents = await file.read()
+        df = pd.read_excel(io.BytesIO(contents), dtype=str) # Read all as string to be safe
+        df.fillna('N/A', inplace=True)
+
+
+        # --- PLZ Column Detection ---
+        temp_plz_col = None
+        for col in df.columns:
+            if 'plz' in col.lower():
+                temp_plz_col = col
+                break
+        
+        if not temp_plz_col:
+            raise HTTPException(status_code=400, detail="No column with 'PLZ' found in the file.")
+
+        plz_column_name = temp_plz_col
+        # Normalize PLZ data
+        df[plz_column_name] = df[plz_column_name].str.strip().str.zfill(5)
+
+
+        # --- Dynamic Filter Detection ---
+        filters = {}
+        for col in df.columns:
+            if col != plz_column_name:
+                unique_values = df[col].unique().tolist()
+                filters[col] = sorted(unique_values)
+
+        df_storage = df
+        
+        return {"filename": file.filename, "filters": filters, "plz_column": plz_column_name}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred while processing the file: {e}")
+
+
+@app.post("/api/heatmap")
+async def get_heatmap_data(request: FilterRequest):
+    global df_storage, plz_column_name
+    if df_storage is None:
+        raise HTTPException(status_code=404, detail="No data available. Please upload a file first.")
+
+    try:
+        filtered_df = df_storage.copy()
+
+        # Apply filters from the request
+        for column, values in request.filters.items():
+            if values: # Only filter if there are values selected
+                filtered_df = filtered_df[filtered_df[column].isin(values)]
+
+        if filtered_df.empty:
+            return []
+
+        # Aggregate data by PLZ
+        plz_counts = filtered_df.groupby(plz_column_name).size().reset_index(name='count')
+
+        # --- Geocoding Step ---
+        # In a real app, this would be a merge/join with a proper geo dataset
+        heatmap_data = []
+        for _, row in plz_counts.iterrows():
+            plz = row[plz_column_name]
+            coords = PLZ_COORDINATES.get(plz)
+            if coords:
+                heatmap_data.append({
+                    "plz": plz,
+                    "lat": coords["lat"],
+                    "lon": coords["lon"],
+                    "count": row["count"]
+                })
+        
+        return heatmap_data
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred while generating heatmap data: {e}")