Update app.py

app.py

@@ -7,10 +7,8 @@ import pandas as pd
 import yfinance as yf
 from huggingface_hub import hf_hub_download
 
-# This forces TensorFlow to only use the CPU.
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
-# --- 1. Download Model and Scalers from Hugging Face Hub ---
 MODEL_REPO = "munem420/stock-forecaster-lstm"
 MODEL_FILENAME = "model_lstm.h5"
 SCALER_FILENAME = "scalers.joblib"
@@ -24,13 +22,11 @@ except Exception as e:
     print(f"❌ Error downloading files: {e}")
     model_path, scalers_path = None, None
 
-# --- 2. Load the Model and Scalers ---
 loaded_model_lstm = None
 loaded_scalers = None
 
 if model_path and os.path.exists(model_path):
     try:
-        # FIX #1: Added custom_objects to handle the 'mse' metric during loading
         loaded_model_lstm = tf.keras.models.load_model(
             model_path,
             custom_objects={"mse": tf.keras.losses.MeanSquaredError()}
@@ -46,8 +42,6 @@ if scalers_path and os.path.exists(scalers_path):
     except Exception as e:
         print(f"❌ Error loading scalers: {e}")
 
-# ... (The rest of the file is unchanged) ...
-
 ticker_to_name = {'ZURVY': 'Zurich Insurance Group AG'}
 
 def get_ticker_from_input(input_name):
@@ -69,7 +63,7 @@ def forecast_stock(input_name, model, scalers_dict, input_width=60):
     if len(data_df) < input_width:
         return f"Error: Not enough historical data. Need {input_width} days, but only have {len(data_df)}."
     recent_data = data_df.tail(input_width)
-    close_prices = recent_data['Close'].values.reshape(-1, 1)
+    close_prices = recent_data['Close'].values.reshape(-input, 1)
     scaler = scalers_dict.get(ticker)
     if not scaler:
         print(f"Warning: No specific scaler found for {ticker}. Using ZURVY's scaler as a fallback.")