Update

app.py

@@ -1,3 +1,5 @@
+import os
+import shutil
 import gradio as gr
 from pathlib import Path
 from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
@@ -80,10 +82,13 @@ def init_leaderboard(dataframe):
         interactive=False,
     )
 
-def upload_file(filepath):
-    name = Path(filepath).name
-    return [gr.UploadButton(visible=False), gr.DownloadButton(label=f"Download {name}", value=filepath, visible=True)]
-
+# def upload_file(file):
+#     UPLOAD_FOLDER = "./data"    
+#     if not os.path.exists(UPLOAD_FOLDER):    
+#         os.mkdir(UPLOAD_FOLDER)    
+    
+#     shutil.copy(file, UPLOAD_FOLDER)    
+#     gr.Info("File Uploaded!!!")  
 
 demo = gr.Blocks(css=custom_css)
 with demo:
@@ -142,12 +147,31 @@ with demo:
                             )
             with gr.Row():
                 gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+            with gr.Row():
+                with gr.Column():
+                    model_name_textbox = gr.Textbox(label="Model name")
+                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+                    output_format = gr.Textbox(label="Output format", placeholder="Out-GEN")
 
             with gr.Row():
                 u = gr.UploadButton("Upload a file", file_count="single")
-                d = gr.DownloadButton("Download the file", visible=False)
 
-                u.upload(upload_file, u, [u, d])
+                u.upload(upload_file, u)
+
+            submit_button = gr.Button("Submit Eval")
+            submission_result = gr.Markdown()
+            submit_button.click(
+                add_new_eval,
+                [
+                    model_name_textbox,
+                    output_format,
+                    revision_name_textbox,
+                    u,
+                ],
+                submission_result,
+            )
+
+
 
                 
 

src/submission/submit.py

@@ -1,6 +1,7 @@
 import json
 import os
 from datetime import datetime, timezone
+import shutil
 
 from src.display.formatting import styled_error, styled_message, styled_warning
 from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO
@@ -15,89 +16,59 @@ REQUESTED_MODELS = None
 USERS_TO_SUBMISSION_DATES = None
 
 def add_new_eval(
-    model: str,
-    base_model: str,
-    revision: str,
-    precision: str,
-    weight_type: str,
-    model_type: str,
+    model_name: str,
+    output_format: str,
+    revision_name: str,
+    upload_file,
 ):
     global REQUESTED_MODELS
     global USERS_TO_SUBMISSION_DATES
     if not REQUESTED_MODELS:
         REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
 
-    user_name = ""
-    model_path = model
-    if "/" in model:
-        user_name = model.split("/")[0]
-        model_path = model.split("/")[1]
-
-    precision = precision.split(" ")[0]
-    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-
-    if model_type is None or model_type == "":
-        return styled_error("Please select a model type.")
+    print(upload_file)
+    
 
     # Does the model actually exist?
     if revision == "":
         revision = "main"
 
-    # Is the model on the hub?
-    if weight_type in ["Delta", "Adapter"]:
-        base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True)
-        if not base_model_on_hub:
-            return styled_error(f'Base model "{base_model}" {error}')
 
-    if not weight_type == "Adapter":
-        model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
-        if not model_on_hub:
-            return styled_error(f'Model "{model}" {error}')
+    file_name = f"{model_name}_{datetime.now()}.json"
+    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
 
-    # Is the model info correctly filled?
-    try:
-        model_info = API.model_info(repo_id=model, revision=revision)
-    except Exception:
-        return styled_error("Could not get your model information. Please fill it up properly.")
+    path = f"{EVAL_REQUESTS_PATH}/{file_name}.json"
 
-    model_size = get_model_size(model_info=model_info, precision=precision)
+    shutil.copy(upload_file, path)
+    print("Uploading eval file")
+    API.upload_file(
+        path_or_fileobj=path,
+        path_in_repo=out_path.split("eval-queue/")[1],
+        repo_id=QUEUE_REPO,
+        repo_type="dataset",
+        commit_message=f"Add {model_name} pred to eval queue",
+    )
 
-    # Were the model card and license filled?
-    try:
-        license = model_info.cardData["license"]
-    except Exception:
-        return styled_error("Please select a license for your model")
+    # Remove the local file
+    os.remove(path)
 
-    modelcard_OK, error_msg = check_model_card(model)
-    if not modelcard_OK:
-        return styled_error(error_msg)
 
     # Seems good, creating the eval
     print("Adding new eval")
 
     eval_entry = {
-        "model": model,
-        "base_model": base_model,
-        "revision": revision,
-        "precision": precision,
-        "weight_type": weight_type,
+        "model": model_name,
+        "revision": revision_name,
         "status": "PENDING",
         "submitted_time": current_time,
-        "model_type": model_type,
-        "likes": model_info.likes,
-        "params": model_size,
-        "license": license,
+        "output_format": output_format,
+        "submission_file": file_name,
         "private": False,
     }
 
-    # Check for duplicate submission
-    if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
-        return styled_warning("This model has been already submitted.")
-
-    print("Creating eval file")
-    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
+    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{version}"
     os.makedirs(OUT_DIR, exist_ok=True)
-    out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"
+    out_path = f"{OUT_DIR}/{model_name}_eval_request_False_{output_format}.json"
 
     with open(out_path, "w") as f:
         f.write(json.dumps(eval_entry))