Update app.py

app.py

@@ -3,23 +3,25 @@ import uuid
 import pandas as pd
 import cv2
 from ultralytics import YOLO
+import gradio as gr
+from huggingface_hub import (
+    create_repo,
+    get_full_repo_name,
+    upload_file,
+)
 
 # Initialize the YOLO model
-model = YOLO('best.pt')
-
-# Define directories
-image_dir = 'datasetsw/valid/ripe'
-output_dir = 'datasetsw/valid/test_ripe'
+model = YOLO('runs/segment/train/weights/best.pt')
+import os
 
-# List all image files in the directory
-image_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.jpg', '.jpeg', '.png'))]
+hf_token = os.environ.get('HUGGINGFACE_TOKEN')
 
 # Initialize an empty list to store the results
 results_list = []
 
 # Process each image
-for image_file in image_files:
-    results = model(image_file)
+def palm_detection(image):
+    results = model(image)
     annotated_frame = results[0].plot()
 
     total_objects = len(results[0].boxes)
@@ -29,22 +31,50 @@ for image_file in image_files:
 
     # Generate a unique filename and save the annotated image
     unique_id = str(uuid.uuid4())
-    combined_filename = f"{unique_id}.jpg"
-    output_path = os.path.join(output_dir, combined_filename)
-    cv2.imwrite(output_path, annotated_frame)
+    filename = f"{unique_id}.jpg"
+    _, buffer = cv2.imencode('.jpg', annotated_frame)
+    binary_image = buffer.tobytes()
+    repo_name = get_full_repo_name(model_id="SawitDetection", token=hf_token)
+    img_file_url = upload_file(
+        path_or_fileobj=binary_image,
+        path_in_repo=filename,
+        repo_id=repo_name,
+        repo_type="space",
+        token=hf_token,
+    )
 
     # Append the results to the list
     results_list.append({
-        'image_file': image_file,
+        'image_file': filename,
         'total_objects': total_objects,
         'matang_count': matang_count,
         'mentah_count': mentah_count
     })
-
-# Convert the results list to a dataframe
-results_df = pd.DataFrame(results_list)
-
-# Save the dataframe to a CSV file
-results_df.to_csv(os.path.join(output_dir, 'detection_results.csv'), index=False)
-
-cv2.destroyAllWindows()
+            
+    results_df = pd.DataFrame(results_list)
+    csv_filename = 'detection_results.csv'
+    csv_output_path = os.path.join(csv_filename)
+    results_df.to_csv(csv_output_path, index=False)
+    csv_file_url = upload_file(
+        path_or_fileobj=csv_output_path,
+        path_in_repo=csv_filename,
+        repo_id=repo_name,
+        repo_type="space",
+        token=hf_token,
+    )
+    return annotated_frame, results_df
+    
+with gr.Blocks(theme = "soft", title="Palm Detectior") as palm_detector:
+    gr.Markdown(
+            """
+            # Palm Detection
+            """)
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image()
+            proccess = gr.Button("Proccess Image")
+            img_outputs = gr.Image(label="Detection Results")
+            outputs = gr.components.Dataframe(type="pandas")
+            proccess.click(fn=palm_detection, inputs=[image], outputs= [img_outputs, outputs])
+    if __name__ == "__main__":
+        palm_detector.launch()