Add the submit function

app.py

@@ -21,6 +21,8 @@ import plotly.express as px
 import plotly.graph_objects as go
 import numpy as np
 
+import requests
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -52,6 +54,63 @@ TASK_METADATA_GENERATIVE = {
     "REL": {"icon": "🔗", "name": "Relation Extraction", "tooltip": ""},
 }
 
+# Function to send a Slack notification for a new model submission for evaluation
+def send_slack_notification(model_name, user_name, user_affiliation):
+    # Insert your Slack webhook URL here
+    webhook_url = "https://hooks.slack.com/services/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"
+
+    # Create the message to be sent to Slack
+    message = {
+        "text": f"New model submission for EVALITA-LLM leaderboard:\n\n"
+                f"**Model Name**: {model_name}\n"
+                f"**User**: {user_name}\n"
+                f"**Affiliation**: {user_affiliation}\n"
+                f"Check out the model on HuggingFace: https://huggingface.co/{model_name}"
+    }
+
+    # Send the message to Slack
+    response = requests.post(webhook_url, json=message)
+
+    # Check if the request was successful and return the appropriate message
+    if response.status_code == 200:
+        return "✅ **Notification sent successfully!**"
+    else:
+        return f"❌ **Failed to send notification**: {response.text}"
+
+
+# Funcion to validate the model submission and send the request for processing
+def validate_and_submit_request(model_name, user_email, user_affiliation):
+    # Check if model name is provided and not empty
+    if not model_name or not model_name.strip():
+        return "❌ **Error:** Model name is required."
+
+    # Check if user email is provided and not empty
+    if not user_email or not user_email.strip():
+        return "❌ **Error:** Email address is required."
+
+    # Validate email format using regex
+    email_regex = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
+    if not re.match(email_regex, user_email.strip()):
+        return "❌ **Error:** Invalid email format. Please enter a valid email address."
+
+    # Check if user affiliation is provided and not empty
+    if not user_affiliation or not user_affiliation.strip():
+        return "❌ **Error:** Affiliation is required."
+
+    # Check if model name follows the correct format (organization/model-name)
+    if "/" not in model_name:
+        return "❌ **Error:** Model name must be in format 'organization/model-name' (e.g., 'microsoft/DialoGPT-medium')."
+
+    # Check if the model name contains only valid characters (letters, numbers, '.', '_', '/', and '-')
+    if not re.match(r'^[a-zA-Z0-9._/-]+$', model_name):
+        return "❌ **Error:** Model name contains invalid characters."
+
+    # If all validations pass, send the Slack notification
+    slack_response = send_slack_notification(model_name.strip(), user_email.strip(), user_affiliation.strip())
+
+    # Return the Slack response (success or failure message)
+    return slack_response
+
 def highlight_best_per_task(df):
     """Add 🟡 symbol next to the maximum value in each task column"""
 
@@ -539,6 +598,79 @@ def create_gradio_interface():
             with gr.TabItem("📝 About"):
                 gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
+
+            # 🚀 Submit a new model to evaluate
+            with gr.TabItem("🚀 Submit"):
+                gr.Markdown("# 📝 Model Evaluation Request", elem_classes="markdown-text")
+                gr.Markdown("""
+                            **Fill out the form below to request evaluation of your model on EVALITA-LLM.**
+
+                            Once submitted, our team will automatically receive a notification. We will evaluate the 
+                            submission’s relevance for both research and commercial purposes, as well as assess its feasibility.
+                            """, elem_classes="markdown-text")
+
+                with gr.Row():
+                    with gr.Column():
+                        # HuggingFace model name field
+                        model_name_input = gr.Textbox(
+                            label="HuggingFace Model Name",
+                            placeholder="e.g., microsoft/DialoGPT-medium",
+                            info="Enter the complete model name as it appears on HuggingFace Hub (organization/model-name)",
+                            elem_id="model-name-input"
+                        )
+
+                        # User email field
+                        user_name_input = gr.Textbox(
+                            label="Your email address",
+                            placeholder="e.g., mario.rossi@example.com",
+                            info="Enter your email address for communication",
+                            elem_id="user-email-input"
+                        )
+
+                        # Affiliation field
+                        user_affiliation_input = gr.Textbox(
+                            label="Affiliation",
+                            placeholder="e.g., University of Milan, Google Research, Freelancer",
+                            info="Enter your affiliation (university, company, organization)",
+                            elem_id="user-affiliation-input"
+                        )
+
+                # Submit button
+                submit_request_button = gr.Button(
+                    "📤 Submit Request",
+                    variant="primary",
+                    elem_id="submit-request-button"
+                )
+
+                # Result area
+                submission_status = gr.Markdown(elem_id="submission-status")
+
+                # Connect button to function
+                submit_request_button.click(
+                    validate_and_submit_request,
+                    inputs=[model_name_input, user_name_input, user_affiliation_input],
+                    outputs=submission_status
+                )
+
+                # Additional information
+                with gr.Accordion("ℹ️ Additional Information", open=False):
+                    gr.Markdown("""
+                                **What happens after submission:**
+                                1. Your request is automatically sent to the EVALITA-LLM team
+                                2. We verify that the model is accessible on HuggingFace
+                                3. We contact you to confirm inclusion in the evaluation
+                                4. The model is added to the evaluation queue
+
+                                **Model requirements:**
+                                - Model must be publicly accessible on HuggingFace Hub
+                                - Must be compatible with the EleutherAI/lm-evaluation-harness framework
+                                - Must have a license that allows evaluation
+
+                                **Evaluation tasks:**
+                                Your model will be evaluated on all tasks: TE, SA, HS, AT, WIC, FAQ, LS, SU, NER, REL.
+                                """, elem_classes="markdown-text")
+
+
             # Separatori
             with gr.TabItem("║", interactive=False):
                 gr.Markdown("", elem_classes="markdown-text")