Update app.py

app.py

@@ -1,45 +1,63 @@
 import gradio as gr
 import cv2
 import numpy as np
+import pandas as pd
 from collections import Counter
 from ultralytics import YOLO
 from huggingface_hub import hf_hub_download
 
-# Download model from Hugging Face repo
-MODEL_PATH = hf_hub_download(
-    repo_id="ibrahim313/Bioengineering_Query_Tool_image_based",
-    filename="best.pt"
-)
+# # Download YOLOv10 model from Hugging Face
+# MODEL_PATH = hf_hub_download(
+#     repo_id="ibrahim313/Bioengineering_Query_Tool_image_based",
+#     filename="best.pt"
+# )
 
-# Load the YOLOv10 model
-model = YOLO(MODEL_PATH)
+# Load the model
+model = YOLO("best.pt")
 
-def predict(image):
-    # Convert the image from BGR to RGB
+def process_image(image):
+    """Detect cells in the image, extract attributes, and return results."""
+    # Convert image to RGB
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     
-    # Perform prediction
+    # Perform detection
     results = model.predict(source=image_rgb, imgsz=640, conf=0.25)
     
-    # Get the annotated image
+    # Get annotated image
     annotated_img = results[0].plot()
     
     # Extract detection data
     detections = results[0].boxes.data if results[0].boxes is not None else []
-    class_names = [model.names[int(cls)] for cls in detections[:, 5]] if len(detections) > 0 else []
-    count = Counter(class_names)
-
-    # Create a string representation of the detections
-    detection_str = ', '.join([f"{name}: {count}" for name, count in count.items()]) if class_names else "No detections"
-
-    return annotated_img, detection_str 
+    if len(detections) > 0:
+        class_names = [model.names[int(cls)] for cls in detections[:, 5]]
+        count = Counter(class_names)
+        detection_str = ', '.join([f"{name}: {count[name]}" for name in count])
+        
+        # Extract cell attributes (position, size, etc.)
+        df = pd.DataFrame(detections.numpy(), columns=["x_min", "y_min", "x_max", "y_max", "confidence", "class"])
+        df["class_name"] = df["class"].apply(lambda x: model.names[int(x)])
+        df["width"] = df["x_max"] - df["x_min"]
+        df["height"] = df["y_max"] - df["y_min"]
+        df["area"] = df["width"] * df["height"]
+        
+        summary = df.groupby("class_name")["area"].describe().reset_index()
+    else:
+        detection_str = "No detections"
+        summary = pd.DataFrame(columns=["class_name", "count", "mean", "std", "min", "25%", "50%", "75%", "max"])
+    
+    return annotated_img, detection_str, summary
 
+# Create Gradio interface
 app = gr.Interface(
-    predict, 
+    fn=process_image,
     inputs=gr.Image(type="numpy", label="Upload an Image"),
-    outputs=[gr.Image(type="numpy", label="Annotated Image"), gr.Textbox(label="Detection Counts")],
-    title="Blood Cell Count", 
-    description="Upload an image and YOLOv10 will detect blood cells."
+    outputs=[
+        gr.Image(type="numpy", label="Annotated Image"),
+        gr.Textbox(label="Detection Counts"),
+        gr.Dataframe(label="Cell Statistics")
+    ],
+    title="Bioengineering Image Analysis Tool",
+    description="Upload an image to detect and analyze bioengineering cells using YOLOv10."
 )
 
-app.launch()
+app.launch()