c

app.py

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+from ultralytics import YOLO  
+import streamlit as st 
+import plotly.graph_objs as go
+from PIL import Image,ImageDraw
+import numpy as np 
+from io import BytesIO
+
+colors = {
+    'Apple Scab Leaf': ['rgb(165, 42, 42)', (165, 42, 42)],  # Brown
+    'Apple leaf': ['rgb(128, 0, 128)', (128, 0, 128)],  # Purple
+    'Apple rust leaf': ['rgb(0, 255, 0)', (0, 255, 0)],  # Green
+    'Bell_pepper leaf spot': ['rgb(255, 215, 0)', (255, 215, 0)],  # Gold
+    'Bell_pepper leaf': ['rgb(139, 69, 19)', (139, 69, 19)],  # Brown
+    'Blueberry leaf': ['rgb(128, 128, 128)', (128, 128, 128)],  # Gray
+    'Cherry leaf': ['rgb(0, 128, 0)', (0, 128, 0)],  # Dark green
+    'Corn Gray leaf spot': ['rgb(255, 0, 0)', (255, 0, 0)],  # Red
+    'Corn leaf blight': ['rgb(255, 165, 0)', (255, 165, 0)],  # Orange
+    'Peach leaf': ['rgb(255, 20, 147)', (255, 20, 147)],  # Pink
+    'Potato leaf early blight': ['rgb(255, 105, 180)', (255, 105, 180)],  # Hot pink
+    'Potato leaf late blight': ['rgb(0, 0, 139)', (0, 0, 139)],  # Dark blue
+    'Potato leaf': ['rgb(218, 112, 214)', (218, 112, 214)],  # Orchid
+    'Raspberry leaf': ['rgb(255, 0, 255)', (255, 0, 255)],  # Magenta
+    'Soyabean leaf': ['rgb(255, 69, 0)', (255, 69, 0)],  # Red-orange
+    'Squash Powdery mildew leaf': ['rgb(0, 255, 255)', (0, 255, 255)],  # Cyan
+    'Strawberry leaf': ['rgb(255, 255, 0)', (255, 255, 0)],  # Yellow
+    'Tomato Early blight leaf': ['rgb(154, 205, 50)', (154, 205, 50)],  # Yellow-green
+    'Tomato Septoria leaf spot': ['rgb(0, 0, 255)', (0, 0, 255)],  # Blue
+    'Tomato leaf bacterial spot': ['rgb(255, 99, 71)', (255, 99, 71)],  # Tomato
+    'Tomato leaf late blight': ['rgb(46, 139, 87)', (46, 139, 87)],  # Sea green
+    'Tomato leaf mosaic virus': ['rgb(255, 192, 203)', (255, 192, 203)],  # Pink
+    'Tomato leaf yellow virus': ['rgb(173, 255, 47)', (173, 255, 47)],  # Green-yellow
+    'Tomato leaf': ['rgb(0, 128, 128)', (0, 128, 128)],  # Teal
+    'Tomato mold leaf': ['rgb(128, 0, 0)', (128, 0, 0)],  # Maroon
+    'Tomato two spotted spider mites leaf': ['rgb(70, 130, 180)', (70, 130, 180)],  # Steel blue
+    'grape leaf black rot': ['rgb(0, 255, 127)', (0, 255, 127)]  # Spring green
+}
+
+frequencies = {
+    'Apple Scab Leaf': 0,
+    'Apple leaf': 0,
+    'Apple rust leaf': 0,
+    'Bell_pepper leaf spot': 0,
+    'Bell_pepper leaf': 0,
+    'Blueberry leaf': 0,
+    'Cherry leaf': 0,
+    'Corn Gray leaf spot': 0,
+    'Corn leaf blight': 0,
+    'Peach leaf': 0,
+    'Potato leaf early blight': 0,
+    'Potato leaf late blight': 0,
+    'Potato leaf': 0,
+    'Raspberry leaf': 0,
+    'Soyabean leaf': 0,
+    'Squash Powdery mildew leaf': 0,
+    'Strawberry leaf': 0,
+    'Tomato Early blight leaf': 0,
+    'Tomato Septoria leaf spot': 0,
+    'Tomato leaf bacterial spot': 0,
+    'Tomato leaf late blight': 0,
+    'Tomato leaf mosaic virus': 0,
+    'Tomato leaf yellow virus': 0,
+    'Tomato leaf': 0,
+    'Tomato mold leaf': 0,
+    'Tomato two spotted spider mites leaf': 0,
+    'grape leaf black rot': 0
+}
+
+model = YOLO("best.pt","v8")
+
+def draw_bboxes(image, bboxes ):
+    draw = ImageDraw.Draw(image)
+    for bbox in bboxes:
+        x1, y1, x2, y2 = bbox[0:4]
+        class_name = names[bbox[5]]
+        color = colors[class_name][1]
+    
+        if class_name in frequencies : 
+            frequencies[class_name] +=1
+        else : 
+            frequencies[class_name] =1   
+        x1,y1,x2,y2 = int(x1),int(y1),int(x2),int(y2)
+        draw.rectangle([(x1, y1), (x2, y2)], outline=color, width=2)
+
+        
+
+
+
+st.title('Plant(Apple,tomato,corn,..) disease detector using yolov8 ')
+image  = st.file_uploader("**put your image for examintion  :** ")
+
+
+if image is not None :
+    image = Image.open(image) 
+    #image = image.filter(ImageFilter.MedianFilter(5))
+    image_np = np.array(image)
+    
+    result = model.predict(source=image_np,conf=0.25,save=False)
+    names =result[0].names 
+    data = result[0].boxes.data.numpy()
+    xyxy = data[:,:]
+    button = st.button("start analyzing .." , type="primary")
+
+    if button : 
+
+        draw_bboxes(image, xyxy)
+        image.save("output.png")
+        
+
+
+
+        x = list(frequencies.values())
+        y = list(frequencies.keys())
+        colors_list = [colors[key][0] for key in y]
+        # Create a bar plot
+        fig = go.Figure(data=[go.Bar(x=y, y=x, marker_color=colors_list)])
+
+        # Display image in the first column
+        
+        st.image("output.png", caption='Annotated Image', use_column_width=True)
+        st.download_button(
+                label="Download image",
+                data=BytesIO(image.tobytes()),
+                file_name="result_image.jpg",
+                key="download_button",
+                help="Click to download t image.",
+            )
+
+        # Display frequencies in the second column
+
+        st.plotly_chart(fig, use_container_width=True)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea545d0d356576c155897287f014db9447dd85f31b8bca5819d97f41ced9bbf1
+size 52064321

requirements.txt

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+numpy==1.24.3
+Pillow==10.1.0
+streamlit==1.29.0
+ultralytics==8.1.10
+plotly==5.18.0