Update app.py

app.py

@@ -5,61 +5,56 @@ import numpy as np
 from prophet import Prophet
 import plotly.graph_objs as go
 import math
-import requests  # For API calls
-from sklearn.ensemble import RandomForestClassifier  # For the model
-from textblob import TextBlob  # For sentiment analysis (optional)
-
-
-# --- Replace with your Alpha Vantage API key ---
-ALPHA_VANTAGE_API_KEY = "YOUR_ALPHA_VANTAGE_API_KEY"  # <--- Replace with your key
+import requests
+from sklearn.ensemble import RandomForestClassifier
+from textblob import TextBlob
+import yfinance as yf  # Import yfinance
 
 # --- Constants ---
-CRYPTO_SYMBOLS = ["BTCUSDT", "ETHUSDT"]
+CRYPTO_SYMBOLS = ["BTC-USD", "ETH-USD"]  # Use standardized symbols
 STOCK_SYMBOLS = ["AAPL", "MSFT"]
-INTERVAL_OPTIONS = ["1h", "60min"]  # Consistent naming
+INTERVAL_OPTIONS = ["1h", "1d"]  # 1h not available for yfinance for stocks; use 1d for stocks.
 
-# --- Data Fetching Functions ---
+# --- Data Fetching Functions (Revised) ---
 def fetch_crypto_data(symbol, interval="1h", limit=100):
-    """Fetch crypto market data from Binance."""
+    """Fetch crypto market data using yfinance (Yahoo Finance)."""
     try:
-        url = f"https://api.binance.com/api/v3/klines"
-        params = {"symbol": symbol, "interval": interval, "limit": limit}
-        response = requests.get(url, params=params)
-        response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
-        data = response.json()
-        df = pd.DataFrame(data, columns=["timestamp", "open", "high", "low", "close", "volume"])
-        df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
-        df[["open", "high", "low", "close", "volume"]] = df[["open", "high", "low", "close", "volume"]].astype(float)
-        return df.dropna()
-    except requests.exceptions.RequestException as e:
-        raise Exception(f"Error fetching crypto data: {e}")
-    except (ValueError, KeyError) as e:
-        raise Exception(f"Error parsing crypto data: {e}")
+        # yfinance uses standardized symbols (e.g., BTC-USD)
+        ticker = yf.Ticker(symbol)
+        # Handle different intervals.  Yahoo Finance has limitations.
+        if interval == "1h":
+          period = "1d"  #  yfinance doesn't support 1h for historical data, so we'll use 1d and resample.
+          df = ticker.history(period=period, interval="1h")
+        elif interval == "1d":
+          df = ticker.history(period="1y", interval=interval) # Get 1 year of data
+        else:
+            raise ValueError("Invalid interval for yfinance.")
+        if df.empty:
+           raise Exception("No data returned from yfinance.")
 
+        df.reset_index(inplace=True)
+        df.rename(columns={"Datetime": "timestamp", "Open": "open", "High": "high", "Low": "low", "Close": "close", "Volume": "volume"}, inplace=True)
+        df = df[["timestamp", "open", "high", "low", "close", "volume"]]  # Select and order columns
+        return df.dropna()
+    except Exception as e:
+        raise Exception(f"Error fetching crypto data from yfinance: {e}")
 
-def fetch_stock_data(symbol, interval="60min"):
-    """Fetch stock market data from Alpha Vantage."""
+def fetch_stock_data(symbol, interval="1d"):  # Simplified for yfinance
+    """Fetch stock market data using yfinance."""
     try:
-        url = f"https://www.alphavantage.co/query"
-        params = {"function": "TIME_SERIES_INTRADAY", "symbol": symbol, "interval": interval,
-                  "apikey": ALPHA_VANTAGE_API_KEY}
-        response = requests.get(url, params=params)
-        response.raise_for_status() # Raise HTTPError for bad responses
-        data = response.json()
-        if "Time Series (60min)" in data: # Check the JSON for the correct key
-            time_series_data = data["Time Series (60min)"]
-            df = pd.DataFrame(time_series_data).T.reset_index()
-            df.columns = ["timestamp", "open", "high", "low", "close", "volume"] # Standardize
-            df["timestamp"] = pd.to_datetime(df["timestamp"])
-            df[["open", "high", "low", "close", "volume"]] = df[["open", "high", "low", "close", "volume"]].astype(float)
-            return df.dropna()
-        else:
-           raise Exception(f"Error: Could not retrieve stock data.  Check API Key, Symbol, and Interval. Response: {data}")
+        ticker = yf.Ticker(symbol)
+        df = ticker.history(period="1y", interval=interval)  # Get 1 year of daily data
+
+        if df.empty:
+            raise Exception("No data returned from yfinance.")
+
+        df.reset_index(inplace=True)
+        df.rename(columns={"Date": "timestamp", "Open": "open", "High": "high", "Low": "low", "Close": "close", "Volume": "volume"}, inplace=True)
+        df = df[["timestamp", "open", "high", "low", "close", "volume"]]
+        return df.dropna()
+    except Exception as e:
+        raise Exception(f"Error fetching stock data from yfinance: {e}")
 
-    except requests.exceptions.RequestException as e:
-        raise Exception(f"Error fetching stock data: {e}")
-    except (ValueError, KeyError) as e:
-        raise Exception(f"Error parsing stock data: {e}")
 
 def fetch_sentiment_data(keyword):  # Placeholder - replace with a real sentiment analysis method
     """Analyze sentiment from social media (placeholder)."""
@@ -70,7 +65,7 @@ def fetch_sentiment_data(keyword):  # Placeholder - replace with a real sentimen
             f"{keyword} is amazing!"
         ]
         sentiments = [TextBlob(tweet).sentiment.polarity for tweet in tweets]
-        return sum(sentiments) / len(sentiments) / len(sentiments) if sentiments else 0  # Avoid ZeroDivisionError
+        return sum(sentiments) / len(sentiments) if sentiments else 0  # Avoid ZeroDivisionError
     except Exception as e:
         print(f"Sentiment analysis error: {e}")
         return 0
@@ -330,7 +325,7 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
     with gr.Row():
         with gr.Column():
             market_type_dd = gr.Radio(label="Market Type", choices=["Crypto", "Stock"], value="Crypto")
-            symbol_dd = gr.Dropdown(label="Symbol", choices=CRYPTO_SYMBOLS, value="BTCUSDT")  # Start with Crypto
+            symbol_dd = gr.Dropdown(label="Symbol", choices=CRYPTO_SYMBOLS, value="BTC-USD")  # Use standardized symbols
             interval_dd = gr.Dropdown(label="Interval", choices=INTERVAL_OPTIONS, value="1h")
             forecast_steps_slider = gr.Slider(label="Forecast Steps", minimum=1, maximum=100, value=24, step=1)
             daily_box = gr.Checkbox(label="Daily Seasonality", value=True)
@@ -354,7 +349,7 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
     # Event Listener to update symbol dropdown based on market type
     def update_symbol_choices(market_type):
         if market_type == "Crypto":
-            return gr.Dropdown(choices=CRYPTO_SYMBOLS, value="BTCUSDT")
+            return gr.Dropdown(choices=CRYPTO_SYMBOLS, value="BTC-USD")
         elif market_type == "Stock":
             return gr.Dropdown(choices=STOCK_SYMBOLS, value="AAPL") # Default to AAPL for stock
         return gr.Dropdown(choices=[], value=None)  # Shouldn't happen, but safety check