Delete package.py

package.py

@@ -1,60 +0,0 @@
-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)
-