Initialize repo

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
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+-----END CERTIFICATE-----

app.py

@@ -116,6 +116,8 @@ import torch.nn.functional as F
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 import os
+import csv
+import tempfile
 
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 MODEL_ID = "dshi01/convnext-tiny-224-7clss"
@@ -152,7 +154,7 @@ def classify_images_batch(files):
       - table: list of rows for Dataframe
     """
     if not files:
-        return [], []
+        return [], [], None
 
     # Keep at most 10
     files = files[:MAX_BATCH]
@@ -170,7 +172,7 @@ def classify_images_batch(files):
             continue
 
     if not pil_images:
-        return [], []
+        return [], [], None
 
     # Batch preprocess + forward
     inputs = processor(images=pil_images, return_tensors="pt")
@@ -200,7 +202,24 @@ def classify_images_batch(files):
             ", ".join(map(str, top3_scores)),
         ])
 
-    return gallery, table_rows
+    # Create CSV for download
+    csv_path = None
+    try:
+        # Write CSV into a temp file inside project dir so Gradio can serve it
+        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", prefix="predictions_", dir=BASE_DIR, mode="w", newline='', encoding='utf-8')
+        writer = csv.writer(tmp)
+        # headers
+        writer.writerow(["filename", "top1_label", "top1_conf", "top3_labels", "top3_confs"])
+        for row in table_rows:
+            writer.writerow(row)
+        tmp.flush()
+        tmp.close()
+        csv_path = tmp.name
+    except Exception:
+        # If CSV can't be created, return None for the file but keep other outputs
+        csv_path = None
+
+    return gallery, table_rows, csv_path
 
 # ---------- UI ----------
 single = gr.Interface(
@@ -221,6 +240,7 @@ batch = gr.Interface(
             label="Predictions Table",
             wrap=True
         )
+        , gr.File(label="Download CSV")
     ],
     title="🌊 BenthicAI - Batch (up to 10)",
     description="Upload multiple images (max 10). Outputs a gallery with captions and a table of top predictions.",

convert_model.py

@@ -0,0 +1,26 @@
+import torch
+from transformers import ConvNextForImageClassification, AutoConfig, AutoImageProcessor
+
+ckpt_path = "./ConvNextmodel.pth"
+base_model = "facebook/convnext-tiny-224"   # the model you started from
+num_labels = 7
+
+# 1️⃣ Load the raw state dict
+state_dict = torch.load(ckpt_path, map_location="cpu")
+if any(k.startswith("module.") for k in state_dict):
+    state_dict = {k.replace("module.", "", 1): v for k, v in state_dict.items()}
+
+# 2️⃣ Rebuild config and model
+config = AutoConfig.from_pretrained(base_model)
+config.num_labels = num_labels
+model = ConvNextForImageClassification(config)
+missing, unexpected = model.load_state_dict(state_dict, strict=True)
+print("Missing:", missing, "Unexpected:", unexpected)
+
+# 3️⃣ Save in HF format
+save_dir = "./convnext-tiny-224-7cls"
+model.save_pretrained(save_dir)
+
+# 4️⃣ Also save processor (for transforms)
+processor = AutoImageProcessor.from_pretrained(base_model)
+processor.save_pretrained(save_dir)

convnext-tiny-224-7clss

@@ -0,0 +1 @@
+Subproject commit 894a78d86e64973931c5a906bdddd472b5007e6f

predictions__kd1zves.csv

@@ -0,0 +1,7 @@
+filename,top1_label,top1_conf,top3_labels,top3_confs
+2022_9817.jpg,Crab,0.9908,"Crab, Whelk, Skate","0.9908, 0.0056, 0.0013"
+2022_9829.jpg,Crab,0.9915,"Crab, Whelk, Roundfish","0.9915, 0.0028, 0.0018"
+2022_9837.jpg,Roundfish,0.989,"Roundfish, Flatfish, Eel","0.989, 0.0089, 0.001"
+2022_9839.jpg,Roundfish,0.9985,"Roundfish, Eel, Flatfish","0.9985, 0.0005, 0.0002"
+2022_9842.jpg,Crab,0.9892,"Crab, Scallop, Skate","0.9892, 0.0053, 0.002"
+2022_9844.jpg,Crab,0.992,"Crab, Skate, Flatfish","0.992, 0.0025, 0.0022"

predictions_bxv4glpg.csv

@@ -0,0 +1,3 @@
+filename,top1_label,top1_conf,top3_labels,top3_confs
+2022_9817.jpg,Crab,0.9908,"Crab, Whelk, Skate","0.9908, 0.0056, 0.0013"
+2022_9829.jpg,Crab,0.9915,"Crab, Whelk, Roundfish","0.9915, 0.0028, 0.0018"

predictions_pwda2f57.csv

@@ -0,0 +1,7 @@
+filename,top1_label,top1_conf,top3_labels,top3_confs
+2022_9817.jpg,Crab,0.9908,"Crab, Whelk, Skate","0.9908, 0.0056, 0.0013"
+2022_9829.jpg,Crab,0.9915,"Crab, Whelk, Roundfish","0.9915, 0.0028, 0.0018"
+2022_9837.jpg,Roundfish,0.989,"Roundfish, Flatfish, Eel","0.989, 0.0089, 0.001"
+2022_9839.jpg,Roundfish,0.9985,"Roundfish, Eel, Flatfish","0.9985, 0.0005, 0.0002"
+2022_9842.jpg,Crab,0.9892,"Crab, Scallop, Skate","0.9892, 0.0053, 0.002"
+2022_9844.jpg,Crab,0.992,"Crab, Skate, Flatfish","0.992, 0.0025, 0.0022"

predictions_x0bfvcix.csv

@@ -0,0 +1,6 @@
+filename,top1_label,top1_conf,top3_labels,top3_confs
+2022_9817.jpg,Crab,0.9908,"Crab, Whelk, Skate","0.9908, 0.0056, 0.0013"
+2022_9829.jpg,Crab,0.9915,"Crab, Whelk, Roundfish","0.9915, 0.0028, 0.0018"
+2022_9837.jpg,Roundfish,0.989,"Roundfish, Flatfish, Eel","0.989, 0.0089, 0.001"
+2022_9839.jpg,Roundfish,0.9985,"Roundfish, Eel, Flatfish","0.9985, 0.0005, 0.0002"
+2022_9842.jpg,Crab,0.9892,"Crab, Scallop, Skate","0.9892, 0.0053, 0.002"