Update app.py

app.py

@@ -1,193 +1,150 @@
-"""
-TALib + mplfinance + Gradio
-- Fetch data ONLY when symbol/date changes
-- Reuse cached data for chart type & pattern changes
-- Clean dashboard layout
-"""
-
 import yfinance as yf
 import pandas as pd
 import numpy as np
 import mplfinance as mpf
 import talib
 import gradio as gr
-from datetime import date
 import os
-from typing import Optional, List
+from datetime import date
 
 # =====================================================
-# TALib pattern mapping
+# CONFIG
 # =====================================================
 
-TALIB_PATTERNS = sorted([n for n in dir(talib) if n.startswith("CDL")])
-PATTERN_DISPLAY_MAP = {n.replace("CDL", ""): n for n in TALIB_PATTERNS}
-DISPLAY_PATTERNS = ["None"] + list(PATTERN_DISPLAY_MAP.keys())
-
 CHART_TYPE_MAP = {
     "Candlestick": "candle",
     "OHLC": "ohlc",
     "Line": "line"
 }
 
+TALIB_PATTERNS = sorted([n for n in dir(talib) if n.startswith("CDL")])
+PATTERN_MAP = {n.replace("CDL", ""): n for n in TALIB_PATTERNS}
+PATTERN_LIST = ["None"] + list(PATTERN_MAP.keys())
+
 # =====================================================
-# Data utilities
+# DATA CLEANING
 # =====================================================
 
-def _normalize_col_name(col) -> str:
-    if isinstance(col, (tuple, list)):
-        return "_".join(str(c) for c in col if c).lower()
-    return str(col).strip().lower()
-
-def _find_best_col(key: str, columns: List[str]) -> Optional[str]:
-    if key in columns:
-        return key
-    for c in columns:
-        if c.endswith("_" + key):
-            return c
-    for c in columns:
-        if key in c:
-            return c
-    return None
-
-def clean_ohlc(df: pd.DataFrame) -> pd.DataFrame:
+def clean_ohlc(df):
     df = df.copy()
-    df.columns = [_normalize_col_name(c) for c in df.columns]
-
-    found = {}
-    for k in ["open", "high", "low", "close"]:
-        col = _find_best_col(k, df.columns)
-        if not col:
-            raise ValueError(f"Missing column: {k}")
-        found[k] = col
-
-    vol_col = _find_best_col("volume", df.columns)
-
-    cols = [found["open"], found["high"], found["low"], found["close"]]
-    if vol_col:
-        cols.append(vol_col)
-
-    df = df[cols].rename(columns={
-        found["open"]: "Open",
-        found["high"]: "High",
-        found["low"]: "Low",
-        found["close"]: "Close",
-        vol_col: "Volume" if vol_col else None
+    df.columns = [c.lower() for c in df.columns]
+
+    df = df.rename(columns={
+        "open": "Open",
+        "high": "High",
+        "low": "Low",
+        "close": "Close",
+        "volume": "Volume"
     })
 
-    df.index = pd.to_datetime(df.index, errors="coerce")
-    df = df.dropna(subset=["Open", "High", "Low", "Close"])
-    df = df.apply(pd.to_numeric, errors="coerce")
+    df.index = pd.to_datetime(df.index)
+    df = df[["Open", "High", "Low", "Close", "Volume"]]
     df = df.dropna().sort_index()
 
+    return df
+
+# =====================================================
+# LOAD DATA (INTERNET ONLY)
+# =====================================================
+
+def load_data(symbol, start, end):
+    if not symbol:
+        return None, "❌ Symbol required"
+
+    df = yf.download(symbol, start=start, end=end, progress=False)
+
     if df.empty:
-        raise ValueError("No valid OHLC data")
+        return None, "❌ No data fetched"
 
-    return df
+    try:
+        df = clean_ohlc(df)
+    except Exception as e:
+        return None, f"❌ Data error: {e}"
+
+    return df, f"✅ Data loaded for {symbol} ({len(df)} rows)"
 
 # =====================================================
-# Pattern detection
+# PATTERN DETECTION
 # =====================================================
 
-def get_pattern_addplots(df, pattern_name):
-    talib_name = PATTERN_DISPLAY_MAP[pattern_name]
-    func = getattr(talib, talib_name)
+def pattern_addplots(df, pattern):
+    if pattern == "None":
+        return []
 
-    o, h, l, c = (
+    func = getattr(talib, PATTERN_MAP[pattern])
+    res = func(
         df["Open"].values,
         df["High"].values,
         df["Low"].values,
-        df["Close"].values,
+        df["Close"].values
     )
 
-    s = pd.Series(func(o, h, l, c), index=df.index)
-    apds = []
+    s = pd.Series(res, index=df.index)
+    aps = []
 
     if (s > 0).any():
         bull = pd.Series(np.nan, index=df.index)
-        bull[s > 0] = df.loc[s > 0, "Low"] * 0.98
-        apds.append(mpf.make_addplot(bull, type="scatter",
-                                     marker="^", markersize=90,
-                                     color="green", alpha=0.85))
+        bull[s > 0] = df["Low"][s > 0] * 0.98
+        aps.append(mpf.make_addplot(
+            bull, type="scatter", marker="^",
+            color="green", markersize=90
+        ))
 
     if (s < 0).any():
         bear = pd.Series(np.nan, index=df.index)
-        bear[s < 0] = df.loc[s < 0, "High"] * 1.02
-        apds.append(mpf.make_addplot(bear, type="scatter",
-                                     marker="v", markersize=90,
-                                     color="red", alpha=0.85))
-    return apds
-
-# =====================================================
-# Step 1: Fetch & cache data (ONLY on symbol/date change)
-# =====================================================
-
-def fetch_stock_data(symbol, start, end):
-    if not symbol:
-        return None, "Symbol required"
-
-    df = yf.download(symbol, start=start, end=end, progress=False)
-    if df.empty:
-        return None, "No data found"
+        bear[s < 0] = df["High"][s < 0] * 1.02
+        aps.append(mpf.make_addplot(
+            bear, type="scatter", marker="v",
+            color="red", markersize=90
+        ))
 
-    try:
-        df_clean = clean_ohlc(df)
-    except Exception as e:
-        return None, str(e)
-
-    return df_clean, f"Data loaded for {symbol}"
+    return aps
 
 # =====================================================
-# Step 2: Render chart (reuse cached data)
+# BUILD CHART (NO INTERNET)
 # =====================================================
 
-def render_chart(df, symbol, chart_type, pattern):
+def build_chart(df, symbol, chart_type, pattern):
     if df is None:
-        return None, "No cached data"
-
-    addplots = []
-    pattern_label = ""
-
-    if chart_type != "Line" and pattern != "None":
-        addplots = get_pattern_addplots(df, pattern)
-        pattern_label = f" | Pattern: {pattern}"
+        return None, "❌ Load data first"
 
+    aps = pattern_addplots(df, pattern)
     mpf_type = CHART_TYPE_MAP[chart_type]
 
     os.makedirs("/tmp", exist_ok=True)
-    path = f"/tmp/{symbol}_{pd.Timestamp.now().strftime('%Y%m%d%H%M%S')}.png"
+    path = f"/tmp/{symbol}_{pd.Timestamp.now().strftime('%H%M%S')}.png"
 
     fig, _ = mpf.plot(
         df,
         type=mpf_type,
-        volume="Volume" in df.columns and mpf_type != "line",
-        addplot=addplots if addplots and mpf_type != "line" else None,
+        volume=(mpf_type != "line"),
+        addplot=aps if mpf_type != "line" else None,
         style="yahoo",
-        title=f"{symbol} • {chart_type}{pattern_label}",
+        title=f"{symbol} | {chart_type} | Pattern: {pattern}",
         figscale=1.7,
         returnfig=True
     )
 
     fig.savefig(path, dpi=150, bbox_inches="tight")
-    return path, "Chart updated"
+    return path, "📊 Chart built successfully"
 
 # =====================================================
-# Gradio UI
+# UI
 # =====================================================
 
-with gr.Blocks(fill_height=True, theme=gr.themes.Soft()) as iface:
-    gr.Markdown(
-        "# 📊 TALib Candlestick Pattern Dashboard\n"
-        "**Optimized data fetching – instant UI updates**"
-    )
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+    gr.Markdown("## 📈 Stock Chart & Candlestick Pattern Analyzer")
 
     cached_df = gr.State(None)
 
     with gr.Row():
-        with gr.Column(scale=1, min_width=320):
+        with gr.Column(scale=1):
             symbol = gr.Textbox(label="Symbol", value="MSFT")
             start = gr.Textbox(label="Start Date", value="2024-01-01")
             end = gr.Textbox(label="End Date", value=date.today().strftime("%Y-%m-%d"))
 
+            load_btn = gr.Button("🌐 Load Data", variant="primary")
+
             chart_type = gr.Dropdown(
                 label="Chart Type",
                 choices=list(CHART_TYPE_MAP.keys()),
@@ -195,33 +152,32 @@ with gr.Blocks(fill_height=True, theme=gr.themes.Soft()) as iface:
             )
 
             pattern = gr.Dropdown(
-                label="Pattern",
-                choices=DISPLAY_PATTERNS,
+                label="Candlestick Pattern",
+                choices=PATTERN_LIST,
                 value="HAMMER"
             )
 
-            load_btn = gr.Button("🔄 Load Data", variant="primary")
+            build_btn = gr.Button("📊 Build Chart", variant="secondary")
+
             status = gr.Textbox(label="Status", interactive=False)
 
         with gr.Column(scale=3):
-            chart = gr.Image(type="filepath", height=720, show_label=False)
+            chart = gr.Image(show_label=False, height=720)
 
-    # Fetch only when symbol/date changes
+    # BUTTON ACTIONS
     load_btn.click(
-        fetch_stock_data,
+        load_data,
         inputs=[symbol, start, end],
         outputs=[cached_df, status]
     )
 
-    # Render chart when options change
-    for trigger in (chart_type, pattern):
-        trigger.change(
-            render_chart,
-            inputs=[cached_df, symbol, chart_type, pattern],
-            outputs=[chart, status]
-        )
+    build_btn.click(
+        build_chart,
+        inputs=[cached_df, symbol, chart_type, pattern],
+        outputs=[chart, status]
+    )
 
-    # Auto-disable pattern for Line chart
+    # Disable pattern for line chart
     chart_type.change(
         lambda ct: gr.update(interactive=(ct != "Line")),
         inputs=chart_type,
@@ -229,4 +185,4 @@ with gr.Blocks(fill_height=True, theme=gr.themes.Soft()) as iface:
     )
 
 if __name__ == "__main__":
-    iface.launch()
+    app.launch()