Create package.py

package.py

@@ -0,0 +1,60 @@
+import requests
+import matplotlib.pyplot as plt
+from PIL import Image
+import csv 
+import os
+
+API_URL = "https://api-inference.huggingface.co/models/OttoYu/Tree-Inspection"
+headers = {"Authorization": "Bearer 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"}
+
+def TreeAI(image_path):
+    def query(filename):
+        with open(filename, "rb") as f:
+            data = f.read()
+        response = requests.post(API_URL, headers=headers, data=data)
+        return response.json()
+
+    output = query(image_path)
+
+    if "error" in output:
+        print("Error:", output["error"])
+    else:
+        for result in output:
+            label = result["label"]
+            confidence = result["score"]
+            print("Prediction:", label, ",", confidence, "%")
+            
+    image = Image.open(image_path)
+    plt.imshow(image)
+    plt.axis('off')
+    plt.show()
+
+
+def TreeAI_Batch(folder_path, output_csv):
+    image_paths = []
+    for filename in os.listdir(folder_path):
+        if filename.endswith((".jpg", ".jpeg", ".png")):
+            image_paths.append(os.path.join(folder_path, filename))
+    
+    num_images = len(image_paths)
+    results = []
+    
+    for i, image_path in enumerate(image_paths):
+        print(f"Processing image {i+1}/{num_images}...")
+        
+        output = query(image_path)
+        
+        if "error" in output:
+            print("Error:", output["error"])
+        else:
+            for result in output:
+                filename = os.path.basename(image_path)
+                label = result["label"]
+                confidence = result["score"]
+                results.append([filename, label, confidence])
+    
+    with open(output_csv, "w", newline="") as csvfile:
+        writer = csv.writer(csvfile)
+        writer.writerow(["Filename", "Prediction", "Confidence"])
+        writer.writerows(results)
+