Add application file

requirements.txt

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+gradio==3.26.0
+opencv-python==4.7.0.72
+numpy==1.24.0
+matplotlib==3.7.1
+scikit-learn==1.3.0
+Pillow==9.4.0

v4.py

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+import gradio as gr
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import MeanShift
+import io
+from PIL import Image
+
+
+def segment_image(image):
+    
+    img = np.array(image)
+    
+    twoDim = img.reshape((-1, 3))
+    
+    twoDim = np.float32(twoDim)
+    
+    mean_shift = MeanShift(bandwidth=30, bin_seeding=True)  
+    mean_shift.fit(twoDim)
+    
+    labels = mean_shift.labels_
+  
+    segmented_image = mean_shift.cluster_centers_[labels].astype(np.uint8)
+    
+    segmented_image = segmented_image.reshape(img.shape)
+    
+    segmented_image_pil = Image.fromarray(segmented_image)
+    return segmented_image_pil
+
+def analyze_health(image):
+    
+    return "Plante en bonne santé, pas de signes de stress ou de maladie."
+
+# Interface Gradio
+def plant_diagnosis(image):
+    
+    segmented = segment_image(image)
+    
+    diagnosis = analyze_health(segmented)
+    
+    return segmented, diagnosis
+
+# Créer l'interface Gradio
+iface = gr.Interface(
+    fn=plant_diagnosis, 
+    inputs=gr.Image(type="pil", label="Téléchargez une image de votre plante"),  
+    outputs=[gr.Image(label="Image Segmentée"), gr.Textbox(label="Diagnostic")],  
+    title="AppleHealth",
+    description="Téléchargez une image de votre plante pour effectuer une analyse de segmentation et obtenir un diagnostic de sa santé.",
+    live=True  
+)
+
+# Lancer l'application Gradio
+iface.launch()