Reaply GUI changes

app.py

@@ -30,6 +30,9 @@ from src.display.utils import (
 from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, TOKEN, QUEUE_REPO, REPO_ID, RESULTS_REPO
 from src.populate import get_evaluation_queue_df, get_leaderboard_df
 from src.submission.submit import add_new_eval
+from captcha.image import ImageCaptcha
+from PIL import Image
+import random, string
 
 
 original_df = None
@@ -41,7 +44,7 @@ def restart_space():
 
 def download_data():
     global original_df
-    global leaderboard_df 
+    global leaderboard_df
     try:
         print(EVAL_REQUESTS_PATH,QUEUE_REPO)
         snapshot_download(
@@ -60,7 +63,7 @@ def download_data():
 
     _, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
     leaderboard_df = original_df.copy()
-        
+
 
 download_data()
 
@@ -78,7 +81,7 @@ def update_table(
     hidden_df: pd.DataFrame,
     columns: list,
     query: str,
-):  
+):
     #filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
     filtered_df = filter_queries(query, hidden_df)
     df = select_columns(filtered_df, columns)
@@ -151,7 +154,15 @@ def validate_upload(input):
             #raise gr.Error("Cannot divide by zero!")
     except:
         raise gr.Error("Cannot parse file")
-    
+
+
+def generate_captcha(width=200, height=150, length=4):
+    text = ''.join(random.choices(string.ascii_uppercase + string.digits, k=length))
+    captcha_obj = ImageCaptcha(width, height)
+    data = captcha_obj.generate(text)
+    image = Image.open(data)
+    return image, text
+
 
 
 demo = gr.Blocks(css=custom_css)
@@ -185,7 +196,7 @@ with demo:
                             elem_id="column-select",
                             interactive=True,
                         )
-                """    
+                """
                 with gr.Column(min_width=320):
                     # with gr.Box(elem_id="box-filter"):
                     filter_columns_type = gr.CheckboxGroup(
@@ -215,7 +226,7 @@ with demo:
                 value=leaderboard_df[
                     [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                     + shown_columns.value
-                    
+
                 ],
                 headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                 datatype=TYPES,
@@ -264,7 +275,7 @@ with demo:
                     gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
                 """
                 with gr.Column():
-                    
+
                     with gr.Accordion(
                         f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
                         open=False,
@@ -287,6 +298,7 @@ with demo:
                                 datatype=EVAL_TYPES,
                                 row_count=5,
                             )
+
                     with gr.Accordion(
                         f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
                         open=False,
@@ -304,62 +316,47 @@ with demo:
 
             with gr.Row():
                 with gr.Column():
-                    model_name_textbox = gr.Textbox(label="Model name")
-                    #precision = gr.Radio(["bfloat16", "float16", "4bit"], label="Precision", info="What precision are you using for inference?")
-                    precision = gr.Dropdown(
-                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
-                        label="Precision",
-                        multiselect=False,
-                        value="other",
-                        interactive=True,
-                        info="What weight precision were you using during the evaluation?"
-                    )
-                    hf_model_id = gr.Textbox(label="Model link (Optional)", info="URL to the model's Hugging Face repository, or it's official website")
-                    contact_email = gr.Textbox(label="Your E-Mail")
-                    file_output = gr.File()
-                    upload_button = gr.UploadButton("Upload json", file_types=['.json'])
-                    upload_button.upload(validate_upload, upload_button, file_output)
-                    """
-                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
-                    model_type = gr.Dropdown(
-                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
-                        label="Model type",
-                        multiselect=False,
-                        value=None,
-                        interactive=True,
-                    )
-                    """
-                """
-                with gr.Column():
-                    precision = gr.Dropdown(
-                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
-                        label="Precision",
-                        multiselect=False,
-                        value="float16",
-                        interactive=True,
-                    )
-                    weight_type = gr.Dropdown(
-                        choices=[i.value.name for i in WeightType],
-                        label="Weights type",
-                        multiselect=False,
-                        value="Original",
-                        interactive=True,
-                    )
-                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
-                """
-            submit_button = gr.Button("Submit Eval", interactive=True)
-            submission_result = gr.Markdown()
-            submit_button.click(
-                fn = add_new_eval,
-                inputs = [
-                    model_name_textbox,
-                    upload_button,
-                    precision,
-                    hf_model_id,
-                    contact_email
-                ],
-                outputs = [submission_result, model_name_textbox, precision, hf_model_id, contact_email],
-            )
+                    with gr.Group():
+                        model_name_textbox = gr.Textbox(label="Model name")
+                        #precision = gr.Radio(["bfloat16", "float16", "4bit"], label="Precision", info="What precision are you using for inference?")
+                        precision = gr.Dropdown(
+                            choices=[i.value.name for i in Precision if i != Precision.Unknown],
+                            label="Precision",
+                            multiselect=False,
+                            value="other",
+                            interactive=True,
+                            info="What weight precision were you using during the evaluation?"
+                        )
+                        hf_model_id = gr.Textbox(label="Model link (Optional)", info="URL to the model's Hugging Face repository, or it's official website")
+                        contact_email = gr.Textbox(label="Your E-Mail")
+                        file_input = gr.File(file_count="single", interactive=True)
+                        #file_input.upload(validate_upload, file_input)
+                        upload_button = gr.UploadButton("Upload json", file_types=['.json'])
+                        upload_button.upload(validate_upload, upload_button, file_input)
+
+                    with gr.Group():
+                        image, text = generate_captcha()
+                        captcha_img = gr.Image(
+                            image,
+                            #container=False,
+                            show_download_button=False,
+                            show_fullscreen_button=False,
+                            show_share_button=False,
+                        )
+                        captcha_input = gr.Textbox(placeholder="Enter the text in the image above", show_label=False, container=False)
+                        submit_button = gr.Button("Submit Eval", interactive=True)
+                        submission_result = gr.Markdown()
+                        submit_button.click(
+                            fn = add_new_eval,
+                            inputs = [
+                                model_name_textbox,
+                                file_input,
+                                precision,
+                                hf_model_id,
+                                contact_email
+                            ],
+                            outputs = [submission_result, model_name_textbox, precision, hf_model_id, contact_email],
+                        )
 
     with gr.Row():
         with gr.Accordion("📙 Citation", open=False):
@@ -374,4 +371,5 @@ with demo:
 #scheduler = BackgroundScheduler()
 #scheduler.add_job(restart_space, "interval", seconds=3600)
 #scheduler.start()
-demo.queue(default_concurrency_limit=40).launch(server_name="0.0.0.0")
+demo.queue(default_concurrency_limit=40).launch(server_name="0.0.0.0")
+

src/submission/submit.py

@@ -83,7 +83,7 @@ def add_new_eval(
 
         if ret['eval_name'] in existing_eval_names:
             print(f"Model name {ret['eval_name']} is used!")
-            return styled_error(f"Model name {ret['eval_name']} is used!")
+            return styled_error(f"Model name {ret['eval_name']} is used!"), "", "", "", ""
 
         out_path = f"{OUT_DIR}/{eval_name}_eval_request.json"