Use threading for python. and make singals from python

WebServer/AIPython/ai-predictor.py

@@ -8,18 +8,20 @@ import features
 import matplotlib
 matplotlib.use("Agg")
 
-def Predict():
+def Predict(Symbol):
     # Define paths (consistent with your previous script)
     SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
     DATA_DIR = os.path.join(SCRIPT_DIR, "data")
     MODEL_PATH = os.path.join(DATA_DIR, "model.keras")
 
     # Pull 1 month of current data to make prediction against | for volatility 20
-    df = yf.download("NVDA", period="2mo", auto_adjust=True)
+    df = yf.download(Symbol, period="2mo", auto_adjust=True)
     if not df.empty:
         df = features.MakeFeatures(df, 1)
         df = features.CleanDF(df)
 
+    print(Symbol)
+
     # Drop our predictor
     df.drop('Target_Close', axis=1, inplace=True)
 
@@ -50,7 +52,15 @@ def Predict():
 
     print(f"Predicted Target_Close: {flat_predictions}")
 
-    return json.dumps(flat_predictions)
+    movement_indicator = 0
+    if (np.mean(flat_predictions) > 0.01):
+        movement_indicator = 1
+    elif (np.mean(flat_predictions) < -0.01):
+        movement_indicator = -1
+    else:
+        movement_indicator = 0
+
+    return movement_indicator
 
 if __name__ == "__main__":
     Predict()

WebServer/Components/Pages/Home.razor

@@ -22,8 +22,17 @@
     <button @onclick="pullandtrain">@button1Text</button>
     <button @onclick="predict">@button2Text</button>
 
-    @foreach(double cur in predictions){
-        <p>@cur</p><br/>
+    @foreach (stockPredictionPair cur in predictions){
+        <div>
+            <h1>Symbol: @cur.Symbol</h1><br />
+        @if (cur.Movement == -1){
+            <p>Sell</p>
+        } else if (cur.Movement == 1){
+            <p>Buy</p>
+        } else{
+            <p>Hold</p>
+        }
+        </div>
     }
 
 </div>
@@ -34,7 +43,13 @@
     string button1Text = "Train AI";
     string button2Text = "Predict AI";
 
-    double[] predictions = {};
+
+    List<stockPredictionPair> predictions = new List<stockPredictionPair>(){
+      new stockPredictionPair(){ Symbol = "AAPL" },
+      new stockPredictionPair(){ Symbol = "AAPL" },
+      new stockPredictionPair(){ Symbol = "TTD" },
+      new stockPredictionPair(){ Symbol = "FUN" }
+    };
 
     async Task pullandtrain(){
         button1Text = "Do not refresh the page. The data is refreshing.";
@@ -46,8 +61,24 @@
     async Task predict(){
         button2Text = "Do not refresh the page. The data is refreshing.";
         await Task.Delay(1);
-        predictions = aiModule.PredictAI();
+        List<Task> threadpool = new List<Task>();
+        foreach(stockPredictionPair cur in predictions){
+            Task thread = new Task(() => {
+                cur.Movement = aiModule.PredictAI(cur.Symbol);
+                Console.WriteLine("Received Signal [" + cur.Symbol + "] : " + cur.Movement);
+            });
+            thread.Start();
+            threadpool.Add(thread);
+        }
+        await Task.WhenAll(threadpool);
         button2Text = "Refresh Completed";
+        StateHasChanged();
+        await Task.Delay(1);
+    }
+
+    class stockPredictionPair{
+        public string Symbol = "";
+        public int Movement = 0;
     }
 
 }

WebServer/Controllers/PythonInterop.cs

@@ -30,13 +30,11 @@ namespace PythonInterop {
             }
         }
 
-        public double[] PredictAI() {
+        public int PredictAI(string StockSymbol) {
             using (Py.GIL()) {
                 dynamic main = Py.Import("ai-predictor");
-                string result = main.Predict();
-                double[]? predictions = JsonSerializer.Deserialize<double[]>(result);
-                double[] nullCasted = predictions != null ? predictions : [];
-                return nullCasted;
+                int result = main.Predict(StockSymbol);
+                return result;
             }
         }