Update app.py

app.py

@@ -4,82 +4,87 @@ import pandas as pd
 import numpy as np
 import io
 import os
-from pathlib import Path
 
 def zoom_at(img, x, y, zoom):
-    # ...existing code...
+    w, h = img.size
+    zoom2 = zoom * 2
+    img = img.crop((x - w / zoom2, y - h / zoom2,
+                    x + w / zoom2, y + h / zoom2))
+    return img.resize((w, h), Image.LANCZOS)
 
-# App title and description
 st.set_page_config(page_title="Cell Explorer", layout="wide")
-st.title("CLL Explorer: Annotation Tool")
+st.title("CLL Explorer - Annotation Tool")
 
 st.markdown("""
-### About this Application
-This tool helps researchers analyze microscope images of blood cells for malaria detection:
-- Upload microscope images
-- Adjust image view with zoom and enhancement controls
-- Detect and measure cells automatically
-- Save analysis results and annotations
-
-**Note**: Cell measurements are in micrometers (µm)
+### About This Application
+This tool assists researchers in analyzing microscope images of blood cells for malaria detection:
+- **Upload** microscope images.
+- **Adjust** image view with zoom and enhancement controls.
+- **Detect** and measure cells automatically.
+- **Save** analysis results and annotations.
 """)
 
-# Create tabs for different functions
-tab1, tab2, tab3 = st.tabs(["Image Analysis", "Detection Results", "Settings"])
-
-with tab1:
-    col1, col2 = st.columns([2,1])
+uploaded_files = st.file_uploader("Upload Images", accept_multiple_files=True, type=["jpg", "png"])
 
-    with col1:
-        uploaded_files = st.file_uploader("Upload Images", accept_multiple_files=True, type=["jpg", "png"])
+if uploaded_files:
+    img_index = st.selectbox("Select Image", range(len(uploaded_files)), format_func=lambda x: uploaded_files[x].name)
+    img_data = uploaded_files[img_index].read()
+    img = Image.open(io.BytesIO(img_data)).resize((500, 500))
 
-        if uploaded_files:
-            img_index = st.selectbox("Select Image", range(len(uploaded_files)))
-            img_data = uploaded_files[img_index].read()
-            img = Image.open(io.BytesIO(img_data)).resize((800, 800))
+    col1, col2 = st.columns([3, 2])
 
-            st.image(img, caption="Original Image", use_column_width=True)
+    with col1:
+        st.image(img, caption="Uploaded Image", use_column_width=True)
 
     with col2:
-        if uploaded_files:
-            st.subheader("Image Controls")
-            x = st.slider("X Position", 0, 800, 400)
-            y = st.slider("Y Position", 0, 800, 400)
-            zoom = st.slider("Zoom Level", 1, 10, 5)
-
-            with st.expander("Enhancement Settings"):
-                contrast = st.slider("Contrast", 0.0, 5.0, 1.0)
-                brightness = st.slider("Brightness", 0.0, 5.0, 1.0)
-                sharpness = st.slider("Sharpness", 0.0, 2.0, 1.0)
-
-            img_zoomed = zoom_at(img, x, y, zoom)
-            img_processed = ImageEnhance.Contrast(img_zoomed).enhance(contrast)
-            img_processed = ImageEnhance.Brightness(img_processed).enhance(brightness)
-            img_processed = ImageEnhance.Sharpness(img_processed).enhance(sharpness)
-
-            st.image(img_processed, caption="Processed View", use_column_width=True)
-
-with tab2:
-    if uploaded_files:
-        st.subheader("Cell Detection Results")
-        # Add your existing cell detection code here
-
-        col1, col2 = st.columns(2)
-        with col1:
-            description = st.text_area("Analysis Notes", "")
-        with col2:
-            if st.button("Save Analysis"):
-                timestamp = pd.Timestamp.now().strftime("%Y%m%d_%H%M%S")
-                save_dir = Path("analysis_results") / timestamp
-                save_dir.mkdir(parents=True, exist_ok=True)
-
-                img_processed.save(save_dir / "processed_image.jpg")
-                with open(save_dir / "analysis_notes.txt", "w") as f:
+        st.subheader("Image Controls")
+        x = st.slider("X Coordinate", 0, 500, 250)
+        y = st.slider("Y Coordinate", 0, 500, 250)
+        zoom = st.slider("Zoom", 1, 10, 5)
+
+        with st.expander("Enhancement Settings"):
+            contrast = st.slider("Contrast", 0.0, 5.0, 1.0)
+            brightness = st.slider("Brightness", 0.0, 5.0, 1.0)
+            sharpness = st.slider("Sharpness", 0.0, 2.0, 1.0)
+
+    processed_col, actions_col = st.columns([3, 2])
+
+    with processed_col:
+        img_zoomed = zoom_at(img, x, y, zoom)
+        img_contrast = ImageEnhance.Contrast(img_zoomed).enhance(contrast)
+        img_bright = ImageEnhance.Brightness(img_contrast).enhance(brightness)
+        img_sharp = ImageEnhance.Sharpness(img_bright).enhance(sharpness)
+        st.image(img_sharp, caption="Processed Image", use_column_width=True)
+
+    with actions_col:
+        save_image = st.checkbox("Save Image")
+        if save_image:
+            img_sharp.save("image-processed.jpg")
+            st.success("Image saved as `image-processed.jpg`")
+
+        with st.expander("Save Options"):
+            description = st.text_area("Describe the image", "")
+            if st.button("Save Description"):
+                with open("saved_image_description.txt", "w") as f:
                     f.write(description)
-                st.success(f"Analysis saved to {save_dir}")
-
-with tab3:
-    st.subheader("Application Settings")
-    st.checkbox("Enable Auto-Detection", value=True)
-    st.selectbox("Measurement Unit", ["Micrometers", "Pixels"])
-    st.number_input("Detection Confidence Threshold", 0.0, 1.0, 0.5)
+                st.success("Description saved as `saved_image_description.txt`")
+
+            if st.button("Save Image Parameters"):
+                params = {
+                    "coordinates_x": x,
+                    "coordinates_y": y,
+                    "zoom": zoom,
+                    "contrast": contrast,
+                    "brightness": brightness,
+                    "sharpness": sharpness
+                }
+                with open("saved_image_parameters.json", "w") as f:
+                    f.write(pd.DataFrame([params]).to_json(orient="records"))
+                st.success("Image parameters saved as `saved_image_parameters.json`")
+
+            if st.button("Rename Files"):
+                file_ext = str(np.random.randint(100))
+                os.rename("image-processed.jpg", f"img_processed{file_ext}.jpg")
+                os.rename("saved_image_parameters.json", f"saved_image_parameters{file_ext}.json")
+                os.rename("saved_image_description.txt", f"saved_image_description{file_ext}.txt")
+                st.success("Files renamed successfully")