revert model back

.gradio/cached_examples/17/indices.csv

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.gradio/cached_examples/28/indices.csv

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.gradio/cached_examples/28/log.csv

@@ -0,0 +1,13 @@
+Model Prediction,timestamp
+"{""label"": ""Mucus"", ""confidences"": [{""label"": ""Mucus"", ""confidence"": 0.3632867634296417}, {""label"": ""Background"", ""confidence"": 0.20581191778182983}, {""label"": ""Smooth Muscle"", ""confidence"": 0.15993033349514008}, {""label"": ""Lymphocytes"", ""confidence"": 0.05527385324239731}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.052241407334804535}]}",2026-08-17 17:20:42.224079
+"{""label"": ""Mucus"", ""confidences"": [{""label"": ""Mucus"", ""confidence"": 0.2888142466545105}, {""label"": ""Smooth Muscle"", ""confidence"": 0.21987244486808777}, {""label"": ""Background"", ""confidence"": 0.19339622557163239}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.07815686613321304}, {""label"": ""Debris"", ""confidence"": 0.05767099931836128}]}",2026-08-17 17:21:10.878495
+"{""label"": ""Smooth Muscle"", ""confidences"": [{""label"": ""Smooth Muscle"", ""confidence"": 0.41677364706993103}, {""label"": ""Adipose"", ""confidence"": 0.26999616622924805}, {""label"": ""Background"", ""confidence"": 0.06960488855838776}, {""label"": ""Mucus"", ""confidence"": 0.06531162559986115}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.046870503574609756}]}",2026-08-17 17:21:12.181611
+"{""label"": ""Mucus"", ""confidences"": [{""label"": ""Mucus"", ""confidence"": 0.4972829222679138}, {""label"": ""Background"", ""confidence"": 0.12184405326843262}, {""label"": ""Smooth Muscle"", ""confidence"": 0.09683744609355927}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.0623493455350399}, {""label"": ""Debris"", ""confidence"": 0.061689186841249466}]}",2026-08-17 17:21:13.582993
+"{""label"": ""Background"", ""confidences"": [{""label"": ""Background"", ""confidence"": 0.9182832837104797}, {""label"": ""Mucus"", ""confidence"": 0.012001528404653072}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.011282394640147686}, {""label"": ""Lymphocytes"", ""confidence"": 0.010236880742013454}, {""label"": ""Normal Colon Mucosa"", ""confidence"": 0.010085840709507465}]}",2026-08-17 17:21:15.957606
+"{""label"": ""Mucus"", ""confidences"": [{""label"": ""Mucus"", ""confidence"": 0.5924761295318604}, {""label"": ""Smooth Muscle"", ""confidence"": 0.11955559253692627}, {""label"": ""Background"", ""confidence"": 0.0762282982468605}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.05226145684719086}, {""label"": ""Lymphocytes"", ""confidence"": 0.04672982543706894}]}",2026-08-17 17:21:20.870717
+"{""label"": ""Mucus"", ""confidences"": [{""label"": ""Mucus"", ""confidence"": 0.5278304219245911}, {""label"": ""Smooth Muscle"", ""confidence"": 0.17604735493659973}, {""label"": ""Background"", ""confidence"": 0.0776616781949997}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.045659247785806656}, {""label"": ""Lymphocytes"", ""confidence"": 0.04397842660546303}]}",2026-08-17 17:21:21.508202
+"{""label"": ""Adipose"", ""confidences"": [{""label"": ""Adipose"", ""confidence"": 0.7484835386276245}, {""label"": ""Smooth Muscle"", ""confidence"": 0.06206844747066498}, {""label"": ""Background"", ""confidence"": 0.054249707609415054}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.024702735245227814}, {""label"": ""Mucus"", ""confidence"": 0.024689581245183945}]}",2026-08-17 17:21:28.091085
+"{""label"": ""Mucus"", ""confidences"": [{""label"": ""Mucus"", ""confidence"": 0.4644416570663452}, {""label"": ""Background"", ""confidence"": 0.13109032809734344}, {""label"": ""Smooth Muscle"", ""confidence"": 0.13060510158538818}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.07063549011945724}, {""label"": ""Lymphocytes"", ""confidence"": 0.058560699224472046}]}",2026-08-17 17:21:28.790322
+"{""label"": ""Adipose"", ""confidences"": [{""label"": ""Adipose"", ""confidence"": 0.5562595129013062}, {""label"": ""Smooth Muscle"", ""confidence"": 0.14078371226787567}, {""label"": ""Background"", ""confidence"": 0.1093922033905983}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.04194163158535957}, {""label"": ""Debris"", ""confidence"": 0.041490957140922546}]}",2026-08-17 17:21:30.486527
+"{""label"": ""Mucus"", ""confidences"": [{""label"": ""Mucus"", ""confidence"": 0.6570587754249573}, {""label"": ""Smooth Muscle"", ""confidence"": 0.07533004134893417}, {""label"": ""Background"", ""confidence"": 0.05949338525533676}, {""label"": ""Colorectal Adenocarcinoma Epithelium"", ""confidence"": 0.051273688673973083}, {""label"": ""Debris"", ""confidence"": 0.04259587824344635}]}",2026-08-17 17:21:33.373855
+"{""label"": ""Mucus"", ""confidences"": [{""label"": ""Mucus"", ""confidence"": 0.28708454966545105}, {""label"": ""Cancer-associated Stroma"", ""confidence"": 0.2425195723772049}, {""label"": ""Lymphocytes"", ""confidence"": 0.09387765824794769}, {""label"": ""Smooth Muscle"", ""confidence"": 0.08705379068851471}, {""label"": ""Debris"", ""confidence"": 0.08193457126617432}]}",2026-08-17 17:24:10.381773

.vscode/settings.json

@@ -1,3 +1,3 @@
 {
-    "python-envs.defaultEnvManager": "ms-python.python:pipenv"
+    "python-envs.defaultEnvManager": "ms-python.python:venv"
 }

app.py

@@ -4,7 +4,7 @@ from torchvision.transforms import v2
 import gradio
 from PIL import Image
 from huggingface_hub import hf_hub_download
-from models.cross_predictor_hybrid import Predictor
+from models.linear_predictor import Predictor
 import numpy
 import os
 
@@ -26,7 +26,7 @@ model = Predictor(n_labels=len(LABELS))
 
 model_file = hf_hub_download(
     repo_id="Hali5/Mae-Model-MedMNIST-Predictor",
-    filename="checkpoints/model_cross_reducer_hybrid_epoch_100.pt"
+    filename="checkpoints/model_linear_v2_epoch_100.pt"
 )
 
 model.load_state_dict(torch.load(model_file,map_location=device))
@@ -39,11 +39,6 @@ tf = v2.Compose([
     v2.ToDtype(torch.float32, scale=True),
 ])
 
-sample_tf = v2.Compose([
-    v2.ToImage(),
-    v2.ToDtype(torch.float32, scale=True),
-])
-
 dataset = numpy.load("test_samples.npz")
 images = dataset["images"]
 labels = dataset["labels"]
@@ -67,13 +62,6 @@ for i in range(number_of_examples):
     label_index = int(labels[i].item() if hasattr(labels[i], 'item') else labels[i])
     val_label_index = int(val_labels[i].item() if hasattr(val_labels[i], 'item') else val_labels[i])
 
-    if hasattr(img_array, "transpose"):
-        img_array = img_array.transpose(1, 2, 0)
-        val_img_arr = val_img_arr.transpose(1, 2, 0)
-    elif hasattr(img_array, "permute"):
-        img_array = img_array.permute(1, 2, 0).cpu().numpy()
-        val_img_arr = val_img_arr.permute(1, 2, 0).cpu().numpy()
-
     if img_array.max() <= 1.0:
         img_array = (img_array * 255).astype(numpy.uint8)
         val_img_arr = (val_img_arr * 255).astype(numpy.uint8)
@@ -94,29 +82,16 @@ for i in range(number_of_examples):
     example_rows_val.append([val_file_path, val_truth_label_text])
 
 @spaces.GPU
-def predict(image):
+def predict(image,truth_labels=True):
     if image is None:
         return None
-    
+
     # uplouded image
     img_tensor = tf(image).unsqueeze(0).to(device)
 
     with torch.no_grad():
         outputs = model(img_tensor)
-        probabilities = torch.nn.functional.softmax(outputs.squeeze(0), dim=0)
-    
-    return {LABELS[i]: float(probabilities[i]) for i in range(len(LABELS))}
-
-@spaces.GPU
-def sample_predict(image,truth_labels=None):
-    if image is None:
-        return None
-    
-    # already preprocessed test/val_samples
-    img_tensor = sample_tf(image).unsqueeze(0).to(device)
-
-    with torch.no_grad():
-        outputs = model(img_tensor)
+        print(outputs.shape)
         probabilities = torch.nn.functional.softmax(outputs.squeeze(0), dim=0)
     
     return {LABELS[i]: float(probabilities[i]) for i in range(len(LABELS))}
@@ -138,7 +113,7 @@ with gradio.Blocks(css=custom_css) as demo:
         with gradio.Row():
             input_img = gradio.Image(height=512, width=512)
             with gradio.Column():
-                output_lbl = gradio.Label(num_top_classes=5)
+                output_lbl = gradio.Label(num_top_classes=9)
                 btn = gradio.Button("Predict")
                 btn.click(fn=predict, inputs=input_img, outputs=output_lbl)
 
@@ -147,15 +122,15 @@ with gradio.Blocks(css=custom_css) as demo:
         
         with gradio.Row():
             with gradio.Column():
-                input_img_ex = gradio.Image(label="Selected Test Image", height=512, width=512)
-                truth_box = gradio.Textbox(label="Ground Truth Label", interactive=False)
-            output_lbl_ex = gradio.Label(num_top_classes=5, label="Model Prediction")
+                input_img_val_ex = gradio.Image(label="Selected Test Image", height=512, width=512)
+                truth_box_val = gradio.Textbox(label="Ground Truth Label", interactive=False)
+            output_lbl_val_ex = gradio.Label(num_top_classes=9, label="Model Prediction")
         
         gradio.Examples(
             examples=example_rows_val, 
-            inputs=[input_img_ex, truth_box],
-            outputs=output_lbl_ex,
-            fn=sample_predict,
+            inputs=[input_img_val_ex, truth_box_val],
+            outputs=output_lbl_val_ex,
+            fn=predict,
             cache_examples=True,
         )
 
@@ -164,15 +139,15 @@ with gradio.Blocks(css=custom_css) as demo:
         
         with gradio.Row():
             with gradio.Column():
-                input_img_ex = gradio.Image(label="Selected Test Image", height=512, width=512)
-                truth_box = gradio.Textbox(label="Ground Truth Label", interactive=False)
-            output_lbl_ex = gradio.Label(num_top_classes=5, label="Model Prediction")
+                input_img_test_ex = gradio.Image(label="Selected Test Image", height=512, width=512)
+                truth_box_test = gradio.Textbox(label="Ground Truth Label", interactive=False)
+            output_lbl_test_ex = gradio.Label(num_top_classes=9, label="Model Prediction")
         
         gradio.Examples(
             examples=example_rows_test, 
-            inputs=[input_img_ex, truth_box],
-            outputs=output_lbl_ex,
-            fn=sample_predict,
+            inputs=[input_img_test_ex, truth_box_test],
+            outputs=output_lbl_test_ex,
+            fn=predict,
             cache_examples=True,
         )
 

val_samples.npz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3085e9bbff8d470ed1375d618232b580ebb7fe8dbd0fd02c8a584e115936a5d6
-size 2618925
+oid sha256:a0b19f9623a259251d6891de276f4c22cd1a24c1ff2c7700f21819262b229879
+size 2646340