Revert GUI changes

app.py

@@ -30,9 +30,6 @@ 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
@@ -156,14 +153,6 @@ def validate_upload(input):
         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)
 with demo:
@@ -298,7 +287,6 @@ with demo:
                                 datatype=EVAL_TYPES,
                                 row_count=5,
                             )
-
                     with gr.Accordion(
                         f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
                         open=False,
@@ -316,47 +304,62 @@ with demo:
 
             with gr.Row():
                 with gr.Column():
-                    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],
-                        )
+                    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.Row():
         with gr.Accordion("📙 Citation", open=False):
@@ -371,4 +374,4 @@ 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")