Update app.py

app.py

@@ -1,45 +1,35 @@
 import gradio as gr
-import torch
+from analyzer import analyze_cells
+import logging
 from PIL import Image
-import torchvision
-from torchvision import transforms
-from analyzer import analyze_image
 
-# Load the pre-trained model (adjust based on your model)
-model = torch.load('cell_detection_model.pth')
-model.eval()
+logging.basicConfig(level=logging.DEBUG)
 
 def analyze_image(uploaded_file):
     try:
-        # Convert uploaded image to a format that the model can process
-        image = Image.open(uploaded_file)
-        transform = transforms.Compose([
-            transforms.ToTensor(),
-        ])
-        image_tensor = transform(image).unsqueeze(0)  # Add batch dimension
-        
-        # Run inference with the model
-        with torch.no_grad():
-            prediction = model(image_tensor)
-
-        # Get the number of cells (boxes detected)
-        cell_count = len(prediction[0]['masks'])  # Mask prediction provides the number of cells
-
-        # Annotate image with predictions
-        annotated_image = image.copy()
-        draw = ImageDraw.Draw(annotated_image)
-        for box in prediction[0]['boxes']:
-            draw.rectangle([box[0], box[1], box[2], box[3]], outline="red")
-
-        # Return the annotated image and the cell count
-        return annotated_image, f"Total Cells Counted: {cell_count}"
+        if uploaded_file is None:
+            return None, "No file uploaded."
+
+        # uploaded_file is a file path string
+        image_path = uploaded_file
+
+        logging.debug(f"Received image path: {image_path}")
+
+        # Analyze the image using your analyzer function
+        count, annotated_path = analyze_cells(image_path)
+
+        # Open the annotated image for display
+        annotated_image = Image.open(annotated_path)
+
+        return annotated_image, f"Total Cells Counted: {count}"
 
     except Exception as e:
+        logging.error(f"Error during image analysis: {e}")
         return None, f"Error analyzing image: {e}"
 
 iface = gr.Interface(
     fn=analyze_image,
-    inputs=gr.File(file_types=[".png", ".jpg", ".jpeg"]),
+    inputs=gr.File(file_types=[".png", ".jpg", ".jpeg"], type="filepath"),
     outputs=[gr.Image(type="pil"), gr.Textbox()],
     title="Microscope Image Analyzer",
     description="Upload a microscope image (PNG or JPG) to detect and count cells."