Update app.py

app.py

@@ -2,8 +2,6 @@
 
 import gradio as gr
 from PIL import Image
-import numpy as np
-
 
 from pipeline import SmartCBC
 
@@ -13,44 +11,70 @@ from pipeline import SmartCBC
 cbc = SmartCBC()   # loads YOLO + classifier once
 
 
+# -------------------------------------------------
+# Helper to normalize Gallery input
+# -------------------------------------------------
+def _normalize_gallery_images(images):
+    """
+    Gradio Gallery (type='pil') can return:
+      - list[PIL.Image]
+      - or list[(PIL.Image, metadata_dict)]
+
+    This function strips metadata if present and returns a flat list of images.
+    """
+    if images is None:
+        return []
+
+    normalized = []
+    for item in images:
+        # Sometimes (media, metadata), sometimes media only
+        if isinstance(item, tuple):
+            media = item[0]
+        else:
+            media = item
+        normalized.append(media)
+    return normalized
+
+
 # -------------------------------------------------
 # Core Gradio wrapper
 # -------------------------------------------------
 def analyze_images(images, age, gender, output_mode):
     """
-    Wrapper for SmartCBC.analyze() or SmartCBC.analyze_batch()
-    depending on whether 1 or multiple images are uploaded.
+    Wrapper for SmartCBC.analyze().
+
+    - Accepts one or multiple images from a Gradio Gallery.
+    - Delegates to SmartCBC.analyze(), which auto-routes to analyze_batch()
+      if a list of images is provided.
+    - Returns either a human-readable text report or full JSON.
     """
+    # No images
     if images is None or len(images) == 0:
         return "Please upload at least one image.", None
 
-    # Gradio Gallery sometimes returns list of tuples: (PIL.Image, metadata)
-    def to_pil_list(img_list):
-        pil_list = []
-
-        for item in img_list:
-            # Gallery may return (media, meta) or just media
-            if isinstance(item, tuple):
-                media = item[0]
-            else:
-                media = item
-    
-            # filepath string
-            if isinstance(media, str):
-                pil = Image.open(media).convert("RGB")
-            # already a PIL Image
-            elif isinstance(media, Image.Image):
-                pil = media.convert("RGB")
-            # numpy array
-            elif isinstance(media, np.ndarray):
-                pil = Image.fromarray(media).convert("RGB")
-            else:
-                raise ValueError(f"Unsupported gallery item type: {type(media)}")
-    
-            pil_list.append(pil)
-    
-        return pil_list
+    # Normalize gallery output (strip metadata if any)
+    img_list = _normalize_gallery_images(images)
+
+    # If single image, send just that; if multiple, send list → analyze_batch
+    if len(img_list) == 1:
+        image_input = img_list[0]
+    else:
+        image_input = img_list
+
+    # Run SmartCBC pipeline
+    result = cbc.analyze(
+        image=image_input,
+        age=age,
+        gender=gender,
+    )
 
+    # Choose output mode
+    if output_mode == "Text Report":
+        # result["report_text"] is produced by build_api_response()
+        return result.get("report_text", "No report generated."), None
+    else:
+        # Show full structured JSON
+        return None, result
 
 
 # -------------------------------------------------
@@ -58,35 +82,40 @@ def analyze_images(images, age, gender, output_mode):
 # -------------------------------------------------
 with gr.Blocks(title="SmartCBC - Multimodal Blood Analysis") as demo:
 
-    gr.Markdown("""
-    # 🩸 SmartCBC — Multimodal AI Blood Smear Analysis  
-    Upload **one or multiple** peripheral smear FOV images and get:  
+    gr.Markdown(
+        """
+    # 🩸 SmartCBC — Multimodal AI Blood Smear Analysis
+
+    Upload **one or multiple** peripheral smear FOV images and get:
     - RBC / WBC / Platelet counts  
     - WBC subtype classification  
     - Aggregated multi-FOV differential  
     - Age-specific reference comparisons  
     - Clinical insights (non-diagnostic)  
-    """)
+    """
+    )
 
     with gr.Row():
         img_in = gr.Gallery(
             label="Upload 1 or Multiple Blood Smear Images (FOVs)",
             columns=3,
             height="auto",
-            allow_preview=True
+            allow_preview=True,
+            type="pil",          # works with gradio==4.44.1
+            file_types=["image"] # ensure only images are selectable
         )
 
         with gr.Column():
             age_in = gr.Number(label="Age (years)", value=30)
             gender_in = gr.Dropdown(
-                ["M", "F", ""],
+                ["", "M", "F"],
                 label="Gender (optional)",
                 value=""
             )
             output_mode = gr.Radio(
                 ["Text Report", "Structured JSON"],
                 value="Text Report",
-                label="Output Format"
+                label="Output Format",
             )
             btn = gr.Button("Analyze")
 
@@ -95,19 +124,19 @@ with gr.Blocks(title="SmartCBC - Multimodal Blood Analysis") as demo:
         label="Report (Human Readable)",
         visible=True,
         lines=30,
-        interactive=False
+        interactive=False,
     )
 
     json_out = gr.JSON(
         label="Structured Output (JSON)",
-        visible=False
+        visible=False,
     )
 
-    # Toggle visibility
+    # Toggle visibility based on output mode
     def toggle_output(mode):
         return (
             gr.update(visible=(mode == "Text Report")),
-            gr.update(visible=(mode == "Structured JSON"))
+            gr.update(visible=(mode == "Structured JSON")),
         )
 
     output_mode.change(toggle_output, [output_mode], [txt_out, json_out])
@@ -116,7 +145,7 @@ with gr.Blocks(title="SmartCBC - Multimodal Blood Analysis") as demo:
     btn.click(
         analyze_images,
         inputs=[img_in, age_in, gender_in, output_mode],
-        outputs=[txt_out, json_out]
+        outputs=[txt_out, json_out],
     )