Update app.py

app.py

@@ -1,57 +1,45 @@
-
 import gradio as gr
+from gradio import components as gc
 import cv2
 import requests
 import os
-
 from ultralyticsplus import YOLO, render_result
 
 # Model Heading and Description
 model_heading = "StockMarket: Trends Recognition for Trading Success"
-description = """ 🌟 Elevate Your Trading Odyssey with Trend Predictions! 🌟
-Dive deep into the enigma of market trends with the precision of a seasoned detective. 🕵️‍♂️ With Foduu AI's unparalleled insights, transition seamlessly from bearish 'Downs' to bullish 'Ups'. 📉📈
-Consider us your trading compass, guiding you through the financial wilderness like a modern-day Gandalf. 🧙‍♂️ Whether you're a seasoned trader or just embarking on your journey, we're here to illuminate your path. 💡
-Trading with us? It's like possessing the secret recipe to investment success. 🍲💰
-Intrigued? Dive into the world of trading alchemy! 🌌
-💌 Reach Out: [email protected]
-👍 Give us a thumbs up and embark on an unparalleled trading escapade! No, you won't gain superpowers, but you'll be one step closer to mastering the markets! 🚀🌍📊!"""
+description = "... (rest of the description) ..."
 
-image_path= [['test/1.jpg', 'foduucom/stockmarket-future-prediction', 640, 0.25, 0.45], ['test/2.jpg', 'foduucom/stockmarket-future-prediction', 640, 0.25, 0.45],['test/3.jpg', 'foduucom/stockmarket-future-prediction', 640, 0.25, 0.45]]
+image_path = [['test/1.jpg', 'foduucom/stockmarket-future-prediction', 640, 0.25, 0.45], ...]
 
 # Load YOLO model
 model = YOLO("foduucom/stockmarket-future-prediction")
 
-
-#############################################################Image Inference############################################################
 def yolov8_img_inference(
-    image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 640,
-    conf_threshold: gr.inputs.Slider = 0.25,
-    iou_threshold: gr.inputs.Slider = 0.45
+    image: gc.Image = None,
+    model_path: str = "foduucom/stockmarket-future-prediction",
+    image_size: gc.Slider = 640,
+    conf_threshold: gc.Slider = 0.25,
+    iou_threshold: gc.Slider = 0.45
 ):
     model = YOLO(model_path)
     model.overrides['conf'] = conf_threshold
-    model.overrides['iou']= iou_threshold
-    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-    model.overrides['max_det'] = 1000 
-    #image = read_image(image)
+    model.overrides['iou'] = iou_threshold
+    model.overrides['agnostic_nms'] = False
+    model.overrides['max_det'] = 1000
     results = model.predict(image)
     render = render_result(model=model, image=image, result=results[0])
-    
     return render
 
-    
 inputs_image = [
-    gr.inputs.Image(type="filepath", label="Input Image"),
-    gr.inputs.Dropdown(["foduucom/stockmarket-future-prediction"], 
-                       default="foduucom/stockmarket-future-prediction", label="Model"),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+    gc.Image(type="filepath", label="Input Image"),
+    gc.Dropdown(["foduucom/stockmarket-future-prediction"], default="foduucom/stockmarket-future-prediction", label="Model"),
+    gc.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    gc.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    gc.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
 ]
 
-outputs_image =gr.outputs.Image(type="filepath", label="Output Image")
+outputs_image = gc.Image(type="filepath", label="Output Image")
+
 interface_image = gr.Interface(
     fn=yolov8_img_inference,
     inputs=inputs_image,
@@ -62,5 +50,6 @@ interface_image = gr.Interface(
     cache_examples=False,
     theme='huggingface'
 )
+
 interface_image.queue()
-interface_image.launch(debug=True)
+interface_image.launch(debug=True)