Update app.py

app.py

@@ -4,63 +4,82 @@ import pandas as pd
 import numpy as np
 import io
 import os
+from pathlib import Path
 
 def zoom_at(img, x, y, zoom):
-    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)
-
-st.title("Cell Explorer")
-
-uploaded_files = st.file_uploader("Upload Images", accept_multiple_files=True, type="jpg")
-
-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((500, 500))
-    x = st.slider("X Coordinate", 0, 500, 205)
-    y = st.slider("Y Coordinate", 0, 500, 250)
-    zoom = st.slider("Zoom", 1, 10, 5)
-    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_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)
-
-    save_image = st.checkbox("Save Image")
-    if save_image:
-        img_sharp.save("image-processed.jpg")
-        st.success("Image saved as image-processed.jpg")
-
-    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("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")
+    # ...existing code...
+
+# App title and description
+st.set_page_config(page_title="Cell Explorer", layout="wide")
+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)
+""")
+
+# Create tabs for different functions
+tab1, tab2, tab3 = st.tabs(["Image Analysis", "Detection Results", "Settings"])
+
+with tab1:
+    col1, col2 = st.columns([2,1])
+
+    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)))
+            img_data = uploaded_files[img_index].read()
+            img = Image.open(io.BytesIO(img_data)).resize((800, 800))
+
+            st.image(img, caption="Original 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:
+                    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)