Create app.py

app.py

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+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)