Update app.py

app.py

@@ -1,79 +1,93 @@
 import gradio as gr
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-import joblib
-from tensorflow.keras.models import load_model
 
-# Load the trained LSTM model
-model = load_model("lstm_gru_model5.h5")
+def message():
+    return "🚧 Under Construction 🚧\n\nCheck back soon!"
 
-# Load the MinMaxScaler
-scaler = joblib.load("forex_scaler222.pkl")
+demo = gr.Interface(
+    fn=message,
+    inputs=[],
+    outputs=gr.Textbox(label="Status"),
+    title="Site Under Construction",
+    description="This application is currently under development. Please check back later!"
+)
+
+demo.launch()
+# import gradio as gr
+# import numpy as np
+# import pandas as pd
+# import tensorflow as tf
+# import joblib
+# from tensorflow.keras.models import load_model
 
-def preprocess_input(data):
-    """Preprocess input data for LSTM model."""
-    if len(data) < 60:
-        mean_value = np.mean(data)  # Compute mean of given prices
-        data = data + [mean_value] * (60 - len(data))  # Fill missing spots
+# # Load the trained LSTM model
+# model = load_model("lstm_gru_model5.h5")
 
-    data = np.array(data).reshape(1, -1)  # Reshape for MinMaxScaler
-    scaled_data = scaler.transform(data)  # Scale the input
-    return scaled_data.reshape(1, 60, 1)  # Reshape for LSTM
+# # Load the MinMaxScaler
+# scaler = joblib.load("forex_scaler222.pkl")
 
+# def preprocess_input(data):
+#     """Preprocess input data for LSTM model."""
+#     if len(data) < 60:
+#         mean_value = np.mean(data)  # Compute mean of given prices
+#         data = data + [mean_value] * (60 - len(data))  # Fill missing spots
 
+#     data = np.array(data).reshape(1, -1)  # Reshape for MinMaxScaler
+#     scaled_data = scaler.transform(data)  # Scale the input
+#     return scaled_data.reshape(1, 60, 1)  # Reshape for LSTM
 
-def predict_forex(prices):
-    """Predict the next forex price based on the input sequence."""
-    try:
-        input_data = [float(price) for price in prices.split(",")]
+
+
+# def predict_forex(prices):
+#     """Predict the next forex price based on the input sequence."""
+#     try:
+#         input_data = [float(price) for price in prices.split(",")]
         
-        # Ensure enough input data
-        if len(input_data) < 60:
-            return "Please provide at least 60 previous forex prices."
+#         # Ensure enough input data
+#         if len(input_data) < 60:
+#             return "Please provide at least 60 previous forex prices."
         
-        # Use last 60 prices
-        preprocessed_data = preprocess_input(input_data[-60:])
-        prediction = model.predict(preprocessed_data)
-
-        # Convert back to original scale
-        predicted_price = scaler.inverse_transform(prediction)[0][0]
-        return f"Predicted Next Price: {predicted_price:.5f}"
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def batch_predict(file):
-    """Batch prediction for CSV files."""
-    try:
-        df = pd.read_csv(file)
-        if "prices" not in df.columns:
-            return "CSV must have a 'prices' column with historical data."
+#         # Use last 60 prices
+#         preprocessed_data = preprocess_input(input_data[-60:])
+#         prediction = model.predict(preprocessed_data)
+
+#         # Convert back to original scale
+#         predicted_price = scaler.inverse_transform(prediction)[0][0]
+#         return f"Predicted Next Price: {predicted_price:.5f}"
+#     except Exception as e:
+#         return f"Error: {str(e)}"
+
+
+# def batch_predict(file):
+#     """Batch prediction for CSV files."""
+#     try:
+#         df = pd.read_csv(file)
+#         if "prices" not in df.columns:
+#             return "CSV must have a 'prices' column with historical data."
         
-        df["predictions"] = df["prices"].rolling(window=10).apply(lambda x: predict_forex(",".join(map(str, x))) if len(x) == 10 else None)
-        return df.dropna()
-    except Exception as e:
-        return f"Error: {str(e)}"
+#         df["predictions"] = df["prices"].rolling(window=10).apply(lambda x: predict_forex(",".join(map(str, x))) if len(x) == 10 else None)
+#         return df.dropna()
+#     except Exception as e:
+#         return f"Error: {str(e)}"
 
-# Gradio UI
-demo = gr.Interface(
-    fn=predict_forex,
-    inputs=gr.Textbox(label="Enter last 10 forex prices (comma-separated)"),
-    outputs=gr.Textbox(label="Predicted Next Price"),
-    title="Forex Price Predictor",
-    description="Enter the last 10 forex prices to predict the next price. Upload CSV for batch predictions.",
-    examples=[
-        ["1.2345,1.2350,1.2360,1.2370,1.2380,1.2390,1.2400,1.2410,1.2420,1.2430"]
-    ],
-    allow_flagging="never"
-)
+# # Gradio UI
+# demo = gr.Interface(
+#     fn=predict_forex,
+#     inputs=gr.Textbox(label="Enter last 10 forex prices (comma-separated)"),
+#     outputs=gr.Textbox(label="Predicted Next Price"),
+#     title="Forex Price Predictor",
+#     description="Enter the last 10 forex prices to predict the next price. Upload CSV for batch predictions.",
+#     examples=[
+#         ["1.2345,1.2350,1.2360,1.2370,1.2380,1.2390,1.2400,1.2410,1.2420,1.2430"]
+#     ],
+#     allow_flagging="never"
+# )
 
-batch_demo = gr.Interface(
-    fn=batch_predict,
-    inputs=gr.File(label="Upload CSV"),
-    outputs=gr.Dataframe(label="Predictions"),
-    title="Batch Prediction",
-    description="Upload a CSV with a 'prices' column for batch predictions."
-)
+# batch_demo = gr.Interface(
+#     fn=batch_predict,
+#     inputs=gr.File(label="Upload CSV"),
+#     outputs=gr.Dataframe(label="Predictions"),
+#     title="Batch Prediction",
+#     description="Upload a CSV with a 'prices' column for batch predictions."
+# )
 
-gr.TabbedInterface([demo, batch_demo], ["Single Prediction", "Batch Prediction"]).launch()
+# gr.TabbedInterface([demo, batch_demo], ["Single Prediction", "Batch Prediction"]).launch()