Upload app (9).py

app (9).py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1vvN4x-mEuGtdgL2zlzu2ci6sercypfCa
+"""
+
+import numpy as np
+import pandas as pd
+import gradio as gr
+from keras.models import Sequential
+from keras.layers import SimpleRNN, Dense
+from sklearn.preprocessing import MinMaxScaler
+
+# Generate dummy stock price data
+def generate_dummy_data():
+    np.random.seed(0)
+    time_steps = 100
+    x = np.linspace(0, 20, time_steps)
+    prices = 50 + np.sin(x) * 10 + np.random.normal(0, 1, time_steps)
+    return prices
+
+# Preprocess data
+def prepare_data(data, window_size):
+    X, y = [], []
+    for i in range(len(data) - window_size):
+        X.append(data[i:i + window_size])
+        y.append(data[i + window_size])
+    return np.array(X), np.array(y)
+
+# Create and train RNN model
+def train_model():
+    raw_prices = generate_dummy_data().reshape(-1, 1)
+    scaler = MinMaxScaler()
+    scaled_prices = scaler.fit_transform(raw_prices)
+
+    window_size = 5
+    X, y = prepare_data(scaled_prices, window_size)
+    X = X.reshape((X.shape[0], X.shape[1], 1))
+
+    model = Sequential([
+        SimpleRNN(50, activation='relu', input_shape=(window_size, 1)),
+        Dense(1)
+    ])
+
+    model.compile(optimizer='adam', loss='mse')
+    model.fit(X, y, epochs=30, verbose=0)
+
+    return model, scaler, window_size
+
+model, scaler, window_size = train_model()
+
+# Prediction function for Gradio
+def predict_next_prices(inputs):
+    inputs = [float(i) for i in inputs.split(',')]
+    if len(inputs) != window_size:
+        return f"Please enter {window_size} comma-separated values."
+
+    input_array = np.array(inputs).reshape(-1, 1)
+    scaled_input = scaler.transform(input_array).reshape((1, window_size, 1))
+    scaled_prediction = model.predict(scaled_input)[0][0]
+    predicted_price = scaler.inverse_transform([[scaled_prediction]])[0][0]
+    return f"Predicted Next Price: ₹{predicted_price:.2f}"
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict_next_prices,
+    inputs=gr.Textbox(label=f"Enter last {window_size} stock prices (comma-separated)"),
+    outputs="text",
+    title="RNN Stock Price Predictor (Dummy Data)",
+    description="Enter previous prices to predict the next value."
+)
+
+if __name__ == "__main__":
+    demo.launch()