app.py

# -*- coding: utf-8 -*-
"""app.py

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1LNqeNTVe-zMc4YfmFi5S5fPJJcv572lx
"""

import gradio as gr
import pickle
import numpy as np

# Load the trained model
with open("stock_model.pkl", "rb") as file:
    model = pickle.load(file)

# Define the prediction function
def predict_stock_price(features):
    try:
        # Convert input string to a NumPy array
        features_array = np.array([float(x) for x in features.split(",")]).reshape(1, -1)
        # Predict using the model
        prediction = model.predict(features_array)
        return f"Predicted Stock Price: {prediction[0]}"
    except Exception as e:
        return f"Error: {e}"

# Create a Gradio Interface
interface = gr.Interface(
    fn=predict_stock_price,  # Function to call
    inputs=gr.Textbox(label="Input Features (comma-separated)", placeholder="1.5, 2.3, 0.7, 5.4"),
    outputs=gr.Textbox(label="Prediction"),
    title="Stock Price Predictor",
    description="Enter features as a comma-separated list to predict stock prices."
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()