app.py

import streamlit as st
from ultralyticsplus import YOLO, render_result
import cv2
import tempfile
from PIL import Image

# Title of the Streamlit app
st.title("Stock Market Future Prediction")

# Instructions
st.write("Upload an image and the model will predict future stock market trends.")

# Upload an image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Save the uploaded file to a temporary location
    with tempfile.NamedTemporaryFile(delete=False) as temp:
        temp.write(uploaded_file.read())
        temp_image_path = temp.name

    # Display the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image', use_column_width=True)

    # Load model
    model = YOLO('foduucom/stockmarket-future-prediction')

    # Set model parameters
    model.overrides['conf'] = 0.25  # NMS confidence threshold
    model.overrides['iou'] = 0.45  # NMS IoU threshold
    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
    model.overrides['max_det'] = 1000  # maximum number of detections per image

    # Perform inference
    results = model.predict(temp_image_path)

    # Display results
    st.write("Prediction Results:")
    st.write(results[0].boxes)

    # Render and display the result
    render = render_result(model=model, image=temp_image_path, result=results[0])
    render_image = Image.fromarray(cv2.cvtColor(render.img, cv2.COLOR_BGR2RGB))
    st.image(render_image, caption='Result Image', use_column_width=True)