Add Api model

api-front/.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "."
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

api-front/main.py

@@ -0,0 +1,124 @@
+from fastapi import FastAPI
+import pickle
+from pydantic import BaseModel
+import numpy as np
+import joblib 
+from datetime import datetime, timedelta
+from fastapi.middleware.cors import CORSMiddleware
+import psycopg2
+
+# model_lasso
+lasso_model = joblib.load('lasso_model.pkl')
+class PredictionRequest(BaseModel):
+    day: int
+    month: int
+    yesterday_price: float
+    forecast_days: int
+
+
+
+# api 
+app = FastAPI()
+
+origins = [
+    "http://localhost",
+    "http://localhost:4200",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+def connexion():
+    try:
+        conn = psycopg2.connect(
+            host="localhost",
+            port = "5432",
+            database="postgres",
+            user="postgres",
+            password="4242"
+        )
+    except psycopg2.Error as e:
+        return {"error": f"Failed to connect to database: {str(e)}"}
+    return conn
+
+
+@app.post("/predict")
+def predict(data: PredictionRequest):
+    day = data.day
+    month = data.month
+    yesterday_price = data.yesterday_price
+    forecast_days = data.forecast_days
+
+    # Connect to the PostgreSQL database
+    conn=connexion()
+
+    # Create the table if it doesn't exist
+    with conn:
+        with conn.cursor() as cur:
+            cur.execute("""
+                CREATE TABLE IF NOT EXISTS predictions (
+                    id SERIAL PRIMARY KEY,
+                    date DATE NOT NULL,
+                    prediction FLOAT NOT NULL
+                )
+            """)
+
+    predictions = []
+    current_day = day
+    current_month = month
+    current_price = yesterday_price
+
+    for _ in range(forecast_days):
+        # Make a prediction using the current inputs
+        prediction = lasso_model.predict([[current_day, current_month, current_price]])
+        predictions.append({"date": datetime(year=2023, month=current_month, day=current_day).strftime("%d/%m/%Y"), "prediction": prediction[0]})
+
+        # Insert the prediction into the PostgreSQL database
+        with conn:
+            with conn.cursor() as cur:
+                try:
+                    cur.execute("""
+                        INSERT INTO postgres (date, prediction) VALUES (%s, %s)
+                    """, (datetime(year=2023, month=current_month, day=current_day), prediction[0]))
+                except psycopg2.Error as e:
+                    return {"error": f"Failed to insert data into database: {str(e)}"}
+
+
+        # Update the inputs 
+        current_date = datetime(year=2023, month=current_month, day=current_day)
+        next_date = current_date + timedelta(days=1)
+        current_day = next_date.day
+        current_month = next_date.month
+        current_price = prediction[0]
+
+    # Close the database connection
+    conn.close()
+
+@app.get("/get_predict")
+def get_predict():
+    conn = connexion()
+    cur = conn.cursor()
+
+    # Execute a SELECT query to retrieve all rows from the 'predictions' table
+    cur.execute("SELECT * FROM predictions")
+
+    # Fetch all rows and print them
+    rows = cur.fetchall()
+    # cur.close()
+    # conn.close()
+
+    return rows
+
+#     # Close the cursor and database connection
+
+if __name__ == '__main__':
+    import uvicorn
+    uvicorn.run(app, host='localhost', port=5000)
+    # python -m uvicorn main:app --reload
+
+

api-front/query.py

@@ -0,0 +1,25 @@
+import psycopg2
+
+# Connect to the PostgreSQL database
+conn = psycopg2.connect(
+     host="localhost",
+    port = "5432",
+    database="postgres",
+    user="postgres",
+    password="4242"
+)
+
+# Create a cursor object to execute queries
+cur = conn.cursor()
+
+# Execute a SELECT query to retrieve all rows from the 'predictions' table
+cur.execute("SELECT * FROM postgres")
+
+# Fetch all rows and print them
+rows = cur.fetchall()
+for row in rows:
+    print(row)
+
+# Close the cursor and database connection
+cur.close()
+conn.close()

src/app/line-chart/line-chart.component.ts

@@ -28,10 +28,10 @@ export class LineChartComponent {
 
     const input =
     {
-      "day": 15,
+      "day": 18,
       "month": 5,
       "yesterday_price": 27639,
-      "forecast_days": 10
+      "forecast_days": 5
     }