Create app.py

app.py

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+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import evaluate 
+import pandas as pd
+import gradio as gr
+import os
+from datasets import load_dataset, Dataset, concatenate_datasets
+import datetime
+
+# Load metrics
+bleu = evaluate.load("bleu")
+meteor = evaluate.load("meteor")
+chrf = evaluate.load("chrf")
+
+# Leaderboard file
+
+scoreDatasetName = "NAMAA-Space/egyptian_leaderboard_scores"
+requestsDatasetName = "NAMAA-Space/egyptian_leaderboard_requests"
+
+
+
+def show_leaderboard():
+    leaderboard = load_dataset(scoreDatasetName, split="train").to_pandas()
+    ## only leave unique with hightst bleu value 
+    leaderboard = leaderboard.groupby("model_name").agg({"bleu": "max", "meteor": "max", "chrf": "max"}).reset_index()
+
+    return leaderboard.sort_values(by="bleu", ascending=False)
+
+def submit_to_leaderboard(model_name, model_type, revision, model_prompt):
+
+    requestsDataset = load_dataset(requestsDatasetName, split="train")
+
+    found = requestsDataset.filter(lambda x: x["model_name"] == model_name)
+
+    if len(found) > 0:
+        return "Model already submitted to the leaderboard"
+
+    newRequest = Dataset.from_list([{
+        "model_name": model_name,
+        "submission_time": pd.Timestamp.now(),
+        "model_type": model_type,
+        "status": "pending",
+        "revision": revision,
+        "prompt": model_prompt
+    }])
+
+    finalRequestsDataset = concatenate_datasets([ requestsDataset , newRequest])
+
+    finalRequestsDataset.push_to_hub(requestsDatasetName, private=True)
+
+    return "Model submitted to the leaderboard"
+
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# English-to-Egyptian Arabic Translation Leaderboard")
+
+    with gr.Row():
+        with gr.Column():
+            model_input = gr.Textbox(label="Model Name", placeholder="Enter the model's Hugging Face name...")
+            ## model type translation or text-generation
+            model_type = gr.Radio(choices=["translation", "text-generation"], label="Model Type")
+
+            revision = gr.Textbox(label="Revision", placeholder="Enter the model's revision...")
+
+            model_prompt = gr.Textbox(label="Model Prompt", placeholder="Enter the model's prompt if it's a text-generation model as prefix...")
+
+            submit_btn = gr.Button("Submit")
+
+            message = gr.Textbox(label="Message")
+
+
+    # leaderboard_btn = gr.Button("Show Leaderboard")/
+    leaderboard_output = gr.DataFrame(label="Leaderboard", value=show_leaderboard())
+
+    submit_btn.click(
+        submit_to_leaderboard,
+        inputs=[model_input, model_type, revision, model_prompt],
+        outputs=[message]
+    )
+
+
+demo.launch()
+