improve front

Les modifications consistent en une mise en forme du front, pour l'instant il manque une partie du back pour que ce soit totalement fonctionnel mais ça peut être testé en local sans problème

backend/main.py

-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from dotenv import load_dotenv
-import os
-
-from db import database, models
-from routers import stats, comments, news
-
-app = FastAPI(docs_url="/api/docs", openapi_url="/api/openapi.json")
-
-# load environment variables
-load_dotenv()
-
-origins = [
-    os.getenv('WEB_ROOT'),
-]
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"]
-)
-
-
-@app.on_event("startup")
-def on_startup():
-    # Database creation
-    models.Base.metadata.create_all(bind=database.engine)
-
-
-# Integration of routers
-app.include_router(stats.router)
-app.include_router(comments.router)
-app.include_router(news.router)
-
-
-"""
-import cv2
-import numpy as np
-import keras
-
-from utils.preprocessing import fix_singular_shape, norm_by_imagenet
-
-
-model = keras.models.load_model('model')
-
-# contours of the zone of a picture that should be analyzed by the model
-contours = {
-    'eiffel': [[70, 370], [420, 720], [1280, 720], [1280, 250], [930, 215], [450, 550], [130, 350]]
-}
-
-masks = {}
-for key, polygon in contours.items():
-    mask = np.zeros((1280, 720, 3), dtype=np.unit8)
-    cv2.fillPoly(mask, [polygon], (255, 255, 255))
-    masks[key] = mask
-
-
-@app.get("/estimate/{id}")
-async def estimate_(id: str) -> float:
-    # img = fetch(...)
-    img = np.zeros((1280, 720, 3))
-    resized_img = cv2.cvtColor(cv2.resize(img, (1280, 720)), cv2.COLOR_BGR2RGB).astype(np.float32)
-    masked_img = cv2.bitwise_and(resized_img, mask[id])
-    treated_img = fix_singular_shape(masked_img, 16)
-    input_image = np.expand_dims(np.squeeze(norm_by_imagenet([treated_img])), axis=0)
-    pred_map = np.squeeze(model.predict(input_image))
-    count_prediction = np.sum(pred_map)
-    return count_prediction
-"""
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from dotenv import load_dotenv
+import os
+
+from db import database, models
+from routers import stats, comments, news
+
+app = FastAPI(docs_url="/api/docs", openapi_url="/api/openapi.json")
+
+# load environment variables
+load_dotenv()
+
+origins = [
+    os.getenv('WEB_ROOT'),
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"]
+)
+
+
+@app.on_event("startup")
+def on_startup():
+    # Database creation
+    models.Base.metadata.create_all(bind=database.engine)
+
+
+# Integration of routers
+app.include_router(stats.router)
+app.include_router(comments.router)
+app.include_router(news.router)
+
+
+
+from db.database import get_db
+from fastapi import Depends
+from sqlalchemy.orm import Session
+from db import schemas
+from typing import List
+
+@app.get('/api/records', response_model=List[schemas.Record])
+async def get_records(place: str, db: Session = Depends(get_db)):
+    return db.query(models.Records).filter(models.Records == place).order_by(models.Records.date.desc()).all()
+
+
+@app.post('/api/records', response_model=schemas.Record)
+async def stats(record: schemas.RecordBase, db: Session = Depends(get_db)):
+    db_record = models.Records(**record.dict())
+    db.add(db_record)
+    db.commit()
+    db.refresh(db_record)
+    return db_record
+
+
+@app.delete('/api/records', response_model=None)
+async def stats(id: str, db: Session = Depends(get_db)):
+    if id == 0:
+        db.query(models.Records).delete()
+    else:
+        db.query(models.Records).filter(models.Records.id == id).delete()
+    db.commit()
+    return
+
+
+"""
+import cv2
+import numpy as np
+import keras
+
+from utils.preprocessing import fix_singular_shape, norm_by_imagenet
+
+
+model = keras.models.load_model('model')
+
+# contours of the zone of a picture that should be analyzed by the model
+contours = {
+    'eiffel': [[70, 370], [420, 720], [1280, 720], [1280, 250], [930, 215], [450, 550], [130, 350]]
+}
+
+masks = {}
+for key, polygon in contours.items():
+    mask = np.zeros((1280, 720, 3), dtype=np.unit8)
+    cv2.fillPoly(mask, [polygon], (255, 255, 255))
+    masks[key] = mask
+
+
+@app.get("/estimate/{id}")
+async def estimate_(id: str) -> float:
+    # img = fetch(...)
+    img = np.zeros((1280, 720, 3))
+    resized_img = cv2.cvtColor(cv2.resize(img, (1280, 720)), cv2.COLOR_BGR2RGB).astype(np.float32)
+    masked_img = cv2.bitwise_and(resized_img, mask[id])
+    treated_img = fix_singular_shape(masked_img, 16)
+    input_image = np.expand_dims(np.squeeze(norm_by_imagenet([treated_img])), axis=0)
+    pred_map = np.squeeze(model.predict(input_image))
+    count_prediction = np.sum(pred_map)
+    return count_prediction
+"""