Update app.py

app.py

@@ -1,21 +1,26 @@
-import cv2
-from tensorflow.keras.models import load_model
-from PIL import Image
-import numpy as np 
-import tensorflow as tf
-import streamlit as st 
-import tempfile
-model = load_model('HandSignClassifierGUD.h5')
-# Open the video file
-f = st.file_uploader("Choose a Video")
-array = ['a','b','c','d','e','f','g','h','i','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y']
-    # Read the video file from the file-like object
-if f is not None:
-    img = Image.open(f)
-    img = img.resize((28,28))
-    img = img.convert('L')
-    img = np.reshape(img,(1,28,28,1))
-    pred = model.predict(img)
-    st.image(img,use_column_width=True)
-    st.write(array[np.argmax(pred)])
-    
+from PIL import Image,ImageDraw,ImageFont
+import numpy as np
+from ultralytics import YOLO
+import streamlit as st
+
+model = YOLO('HandSignDetector.pt')
+font_size = 40
+img = st.file_uploader('Choose an Image')
+font = ImageFont.truetype("arial.ttf", size=font_size)
+arr = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
+
+if img is not None:
+    img = Image.open(img).resize((416,416)).convert('RGB')
+    results = model(img)
+    for result in results:
+        for i in range(len(result.boxes.cls)):
+          cls = result.boxes.cls[i]
+          cls = arr[int(cls)]
+          lbl = result.boxes.conf[i]
+          boxes = result.boxes.xyxy[i]
+          draw = ImageDraw.Draw(img)
+          draw.rectangle([boxes[0], boxes[1], boxes[2], boxes[3]], outline="white", width=5)
+          text_position = (boxes[0]+boxes[2])/2, boxes[1]-10
+          draw.text(text_position, f'{cls} {lbl}', fill="red", font=font)
+
+    st.image(img,'Detected')