Delete app.py

app.py

@@ -1,218 +0,0 @@
-################### PACKAGES
-import pandas as pd
-import numpy as np
-from datetime import datetime, timedelta
-import time
-import requests
-from pytrends.request import TrendReq
-import joblib
-import gradio as gr
-from sklearn.preprocessing import StandardScaler
-
-
-################### MODEL LOADING
-# Load XGBoost and NGBoost
-xgb_artifact = joblib.load("xgb_volatility_model.joblib")
-xgb_model = xgb_artifact["model"]
-xgb_features = xgb_artifact["feature_names"]
-
-ngb_artifact = joblib.load("ngb_volatility_model.joblib")
-ngb_model = ngb_artifact["model"]
-ngb_features = ngb_artifact["feature_names"]
-
-forecast_ngb_artifact = joblib.load("Forecast_ngb_volatility_model.joblib")
-forecast_ngb_model = forecast_ngb_artifact["model"]
-forecast_ngb_features = forecast_ngb_artifact["feature_names"]
-
-# Load KMeans + scaler
-kmeans_artifact = joblib.load("kmeans_model.joblib")
-kmeans_model = kmeans_artifact["model"]
-cluster_scaler = kmeans_artifact["scaler"]
-# Only use the features that were actually used during training
-cluster_features_names = ["close", "volume", "trend", "fg_index"]
-
-
-################### FEATURE GATHERING
-def fetch_ohlcv_last_n_days(date="2026-01-18", n_days=90):
-    end = pd.to_datetime(date)
-    start = end - timedelta(days=n_days)
-
-    url = "https://api.coingecko.com/api/v3/coins/bitcoin/market_chart/range"
-    params = {
-        "vs_currency": "usd",
-        "from": int(start.timestamp()),
-        "to": int(end.timestamp())
-    }
-
-    r = requests.get(url, params=params, timeout=20)
-    r.raise_for_status()
-    data = r.json()
-
-    prices = pd.DataFrame(data["prices"], columns=["timestamp", "close"])
-    volumes = pd.DataFrame(data["total_volumes"], columns=["timestamp", "volume"])
-
-    df = prices.merge(volumes, on="timestamp")
-    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
-    df.set_index("timestamp", inplace=True)
-
-    df = df.resample("1D").agg(
-        open=("close", "first"),
-        high=("close", "max"),
-        low=("close", "min"),
-        close=("close", "last"),
-        volume=("volume", "sum")
-    ).dropna()
-
-    return df
-
-def fetch_fg_last_6_days(df):
-    url = "https://api.alternative.me/fng/?limit=0&format=json"
-    response = requests.get(url)
-    fg_df = pd.DataFrame(response.json()["data"])
-    fg_df["timestamp"] = pd.to_datetime(fg_df["timestamp"], unit="s")
-    fg_df.set_index("timestamp", inplace=True)
-    fg_df = fg_df[["value"]].astype(float)
-    fg_df.rename(columns={"value":"fg_index"}, inplace=True)
-    fg_df = fg_df.reindex(df.index, method="ffill")
-    df["fg_index"] = fg_df["fg_index"].values
-    return df
-
-def fetch_google_trend_for_date(keyword="Bitcoin", target_date="2024-01-15", window=7):
-    pytrends = TrendReq(hl="en-US", tz=360)
-    target_date = pd.to_datetime(target_date)
-    start_date = (target_date - timedelta(days=window)).strftime("%Y-%m-%d")
-    end_date = target_date.strftime("%Y-%m-%d")
-    timeframe = f"{start_date} {end_date}"
-    try:
-        pytrends.build_payload([keyword], timeframe=timeframe)
-        df = pytrends.interest_over_time()
-        if df.empty:
-            return 0
-        df = df.rename(columns={keyword: "trend"})
-        df = df.drop(columns=["isPartial"], errors="ignore")
-        df.index = pd.to_datetime(df.index)
-        if target_date in df.index:
-            return df.loc[target_date, "trend"]
-        else:
-            return 0
-    except:
-        return 0
-
-
-################### FEATURE ENGINEERING
-def engineer_features(df):
-    df["log_return"] = np.log(df["close"] / df["close"].shift(1))
-    df["hl_spread"] = df["high"] - df["low"]
-    df["co_spread"] = df["close"] - df["open"]
-    df["momentum_3"] = df["close"] - df["close"].shift(3)
-    df["vol_change"] = df["volume"] - df["volume"].shift(1)
-    df["rolling_std_5"] = df["log_return"].rolling(5).std()
-    # Fill NaNs for first few rows
-    df = df.fillna(0)
-    return df
-
-################### ASSIGN CLUSTER
-def assign_cluster(df):
-    cluster_features = df[cluster_features_names]
-    scaled = cluster_scaler.transform(cluster_features)
-    df["cluster"] = kmeans_model.predict(scaled)
-    return df
-
-################### PREDICTION FUNCTION
-def predict_volatility(date):
-    # Fetch raw data
-    df = fetch_ohlcv_last_n_days(date=date, n_days=90)
-    df = fetch_fg_last_6_days(df)
-    trend_value = fetch_google_trend_for_date("Bitcoin", date)
-    df["trend"] = trend_value
-    df["trend"] = df["trend"].ffill().fillna(0)
-
-    # Feature engineering
-    df = engineer_features(df)
-
-    # Clustering
-    # Clustering
-    df = assign_cluster(df)
-
-    assert "cluster" in df.columns, "Cluster feature missing after assignment"
-
-
-    # ---------------- NOWCASTING ----------------
-    X_xgb = df.iloc[-1:][xgb_features]
-    X_ngb = df.iloc[-1:][ngb_features]
-
-    point = xgb_model.predict(X_xgb)[0]
-    dist = ngb_model.pred_dist(X_ngb)
-    low, high = dist.ppf([0.025, 0.975])
-
-    # ---------------- FORECASTING (t+1) ----------------
-    X_ngb_fore = df.iloc[-1:][forecast_ngb_features]
-
-    for_point = forecast_ngb_model.predict(X_ngb_fore)[0]
-    for_dist = forecast_ngb_model.pred_dist(X_ngb_fore)
-    for_low, for_high = for_dist.ppf([0.025, 0.975])
-
-    return point, low, high, for_point, for_low, for_high
-
-
-
-################### GRADIO INTERFACE
-import gradio as gr
-import pandas as pd
-
-################### HELPER FUNCTION TO RETURN TABLE ###################
-def predict_volatility_for_table(date):
-    """
-    Returns a DataFrame with Nowcast and Forecast predictions for the given date.
-    """
-    # Run your existing predict_volatility function
-    point, low, high, for_point, for_low, for_high = predict_volatility(date)
-
-    # Create a DataFrame to display nicely
-    data = {
-        "Type": ["Nowcast (t)", "Forecast (t+1)"],
-        "Volatility": [point, for_point],
-        "Low 95% CI": [low, for_low],
-        "High 95% CI": [high, for_high]
-    }
-
-    df = pd.DataFrame(data)
-
-    # Round values for better readability
-    df[["Volatility", "Low 95% CI", "High 95% CI"]] = df[["Volatility", "Low 95% CI", "High 95% CI"]].round(4)
-
-    return df
-
-################### GRADIO WRAPPER ###################
-def gradio_predict(date):
-    """
-    Wrapper for Gradio. Returns a DataFrame for display.
-    """
-    # Validate date format
-    try:
-        pd.to_datetime(date)
-    except:
-        return pd.DataFrame({"Error": ["❌ Invalid date format. Use YYYY-MM-DD."]})
-
-    # Attempt to predict
-    try:
-        df = predict_volatility_for_table(date)
-        return df
-    except Exception as e:
-        return pd.DataFrame({"Error": [f"⚠️ Error while computing prediction:\n{str(e)}"]})
-
-################### GRADIO INTERFACE ###################
-demo = gr.Interface(
-    fn=gradio_predict,
-    inputs=gr.Textbox(label="📆 Date (YYYY-MM-DD)"),
-    outputs=gr.Dataframe(label="Volatility Predictions", headers=["Type", "🎯 Volatility", "Low 95% CI", "High 95% CI"]),
-    title="BTC Volatility Predictor",
-    description=(
-        "Enter a date to get predicted BTC volatility and 95% confidence intervals.\n"
-        "Nowcast = today's volatility, Forecast = next day's volatility."
-    )
-)
-
-if __name__ == "__main__":
-    demo.launch()
-