Update app.py

app.py

@@ -1,4 +1,5 @@
-import gradio as gr
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
 from vnstock import Vnstock
 from datetime import datetime, timedelta
 import pandas as pd
@@ -8,11 +9,19 @@ import torch
 from torch_geometric.data import Data
 
 vn = Vnstock()
+app = FastAPI(title="mFund VNStock API", version="1.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 # ============================
-# TECHNICAL INDICATORS
+# Chỉ báo kỹ thuật
 # ============================
-
 def calc_RSI(series, period=14):
     delta = series.diff()
     gain = delta.clip(lower=0)
@@ -35,9 +44,8 @@ def calc_bollinger(series, window=20, num_std=2):
     return sma, sma + num_std * std, sma - num_std * std
 
 # ============================
-# GNN MODEL
+# Mô hình GNN đơn giản
 # ============================
-
 class StockGCN(torch.nn.Module):
     def __init__(self, num_features, hidden=16):
         super().__init__()
@@ -51,19 +59,19 @@ class StockGCN(torch.nn.Module):
         return self.conv2(x, edge)
 
 # ============================
-# API METHODS
+# 1) GET /stock/history
 # ============================
-
-def api_history(symbol, start, end):
+@app.get("/stock/history")
+def get_history(symbol: str, start: str, end: str):
     try:
         if end < start:
-            return {"error": "End date must be >= start date"}
+            return {"error": "end must be >= start"}
 
         stock = vn.stock(symbol=symbol)
         df = stock.quote.history(start=start, end=end, interval="1D")
 
         if df is None or df.empty:
-            return {"error": "No data"}
+            return {"error": "no data"}
 
         if "time" in df.columns:
             df = df.rename(columns={"time": "Date"}).set_index("Date")
@@ -72,48 +80,62 @@ def api_history(symbol, start, end):
             df = df.rename(columns={"close": "Close"})
 
         df.index = df.index.astype(str)
-
         return {"symbol": symbol, "data": df.to_dict()}
-
     except Exception as e:
         return {"error": str(e)}
 
-def api_ta(symbol, start, end):
+# ============================
+# 2) GET /stock/ta
+# ============================
+@app.get("/stock/ta")
+def get_ta(symbol: str, start: str, end: str):
     try:
         if end < start:
-            return {"error": "End date must be >= start date"}
+            return {"error": "end must be >= start"}
 
         stock = vn.stock(symbol=symbol)
         df = stock.quote.history(start=start, end=end, interval="1D")
 
+        if df is None or df.empty:
+            return {"error": "no data"}
+
         df["RSI"] = calc_RSI(df["close"])
         df["MACD"], df["MACD_signal"], df["MACD_hist"] = calc_MACD(df["close"])
         df["BB_MID"], df["BB_UPPER"], df["BB_LOWER"] = calc_bollinger(df["close"])
 
         df = df.fillna(None)
         df.index = df.index.astype(str)
-
         return {"symbol": symbol, "indicators": df.to_dict()}
-
     except Exception as e:
         return {"error": str(e)}
 
-def api_gnn(symbol, days):
+# ============================
+# 3) GET /stock/gnn
+# ============================
+@app.get("/stock/gnn")
+def get_gnn(symbol: str, days: int = 7):
     try:
         end = datetime.today()
         start = end - timedelta(days=365)
 
         stock = vn.stock(symbol=symbol)
-        df = stock.quote.history(start=start.strftime("%Y-%m-%d"),
-                                 end=end.strftime("%Y-%m-%d"),
-                                 interval="1D")
-        df = df.rename(columns={"close": "Close"}).dropna()
+        df = stock.quote.history(
+            start=start.strftime("%Y-%m-%d"),
+            end=end.strftime("%Y-%m-%d"),
+            interval="1D"
+        )
 
+        if df is None or df.empty:
+            return {"error": "no data"}
+
+        df = df.rename(columns={"close": "Close"}).dropna()
         scaler = MinMaxScaler()
         df_scaled = scaler.fit_transform(df[["Close"]])
 
-        edge_index = torch.tensor([[i, i+1] for i in range(len(df_scaled)-1)],
-                                  dtype=torch.long).t()
+        edge_index = torch.tensor(
+            [[i, i + 1] for i in range(len(df_scaled) - 1)],
+            dtype=torch.long
+        ).t()
 
         x = torch.tensor(df_scaled, dtype=torch.float)
         data_obj = Data(x=x, edge_index=edge_index)
@@ -128,7 +150,7 @@ def api_gnn(symbol, days):
             new_obj = Data(
                 x=torch.cat([data_obj.x, last_value]),
                 edge_index=torch.tensor(
-                    [[i, i+1] for i in range(len(data_obj.x))],
+                    [[i, i + 1] for i in range(len(data_obj.x))],
                     dtype=torch.long
                 ).t()
             )
@@ -136,9 +158,14 @@ def api_gnn(symbol, days):
             last_value = out[-1].view(1, 1)
             preds_scaled.append(last_value.item())
 
-        preds_real = scaler.inverse_transform(np.array(preds_scaled).reshape(-1, 1)).flatten()
+        preds_real = scaler.inverse_transform(
+            np.array(preds_scaled).reshape(-1, 1)
+        ).flatten()
 
-        dates = [(end + timedelta(days=i+1)).strftime("%Y-%m-%d") for i in range(days)]
+        dates = [
+            (end + timedelta(days=i + 1)).strftime("%Y-%m-%d")
+            for i in range(days)
+        ]
 
         return {
             "symbol": symbol,
@@ -146,41 +173,21 @@ def api_gnn(symbol, days):
             "predictions": [
                 {"date": d, "price": float(p)}
                 for d, p in zip(dates, preds_real)
-            ]
+            ],
         }
-
     except Exception as e:
         return {"error": str(e)}
 
 # ============================
-# GRADIO APP (API ENABLED)
+# Root
 # ============================
-
-with gr.Blocks() as app:
-    gr.Markdown("# 📈 VNStock API (Gradio on HuggingFace)")
-
-    with gr.Tab("History API"):
-        sym = gr.Text(label="Symbol")
-        start = gr.Text(label="Start (YYYY-MM-DD)")
-        end = gr.Text(label="End (YYYY-MM-DD)")
-        out = gr.JSON(label="Result")
-        btn = gr.Button("Get History")
-        btn.click(api_history, [sym, start, end], out).api_name = "api_history"
-
-    with gr.Tab("Technical Analysis API"):
-        sym2 = gr.Text(label="Symbol")
-        start2 = gr.Text(label="Start (YYYY-MM-DD)")
-        end2 = gr.Text(label="End (YYYY-MM-DD)")
-        out2 = gr.JSON(label="Result")
-        btn2 = gr.Button("Get Indicators")
-        btn2.click(api_ta, [sym2, start2, end2], out2).api_name = "api_ta"
-
-    with gr.Tab("GNN Prediction API"):
-        sym3 = gr.Text(label="Symbol")
-        days3 = gr.Number(label="Days to Predict", value=7)
-        out3 = gr.JSON(label="Result")
-        btn3 = gr.Button("Predict GNN")
-        btn3.click(api_gnn, [sym3, days3], out3).api_name = "api_gnn"
-
-# 🚀 RUN ON HUGGINGFACE — KHÔNG share=True
-app.launch()
+@app.get("/")
+def root():
+    return {
+        "message": "mFund VNStock FastAPI is running",
+        "endpoints": [
+            "/stock/history?symbol=FPT&start=2023-01-01&end=2023-12-31",
+            "/stock/ta?symbol=HPG&start=2023-01-01&end=2023-12-31",
+            "/stock/gnn?symbol=VNM&days=7",
+        ],
+    }