Update app.py

app.py

@@ -1,48 +1,238 @@
 
+# # demo.launch()
+# import gradio as gr
+# import pandas as pd
+# import os
+# import re
+# from datetime import datetime
+
+# LEADERBOARD_FILE = "leaderboard.csv"  # File to store all submissions persistently
+# LAST_UPDATED = datetime.now().strftime("%B %d, %Y")
+
+# def initialize_leaderboard_file():
+#     """
+#     Ensure the leaderboard file exists and has the correct headers.
+#     """
+#     if not os.path.exists(LEADERBOARD_FILE):
+#         # Create the file with headers
+#         pd.DataFrame(columns=[
+#             "Model Name", "Overall Accuracy", "Valid Accuracy",
+#             "Correct Predictions", "Total Questions", "Timestamp"
+#         ]).to_csv(LEADERBOARD_FILE, index=False)
+#     else:
+#         # Check if the file is empty and write headers if needed
+#         if os.stat(LEADERBOARD_FILE).st_size == 0:
+#             pd.DataFrame(columns=[
+#                 "Model Name", "Overall Accuracy", "Valid Accuracy",
+#                 "Correct Predictions", "Total Questions", "Timestamp"
+#             ]).to_csv(LEADERBOARD_FILE, index=False)
+
+# def clean_answer(answer):
+#     """
+#     Clean and normalize the predicted answers.
+#     """
+#     if pd.isna(answer):
+#         return None
+#     answer = str(answer)
+#     clean = re.sub(r'[^A-Da-d]', '', answer)
+#     if clean:
+#         return clean[0].upper()
+#     return None
+
+# def update_leaderboard(results):
+#     """
+#     Append new submission results to the leaderboard file.
+#     """
+#     new_entry = {
+#         "Model Name": results['model_name'],
+#         "Overall Accuracy": round(results['overall_accuracy'] * 100, 2),
+#         "Valid Accuracy": round(results['valid_accuracy'] * 100, 2),
+#         "Correct Predictions": results['correct_predictions'],
+#         "Total Questions": results['total_questions'],
+#         "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+#     }
+
+#     new_entry_df = pd.DataFrame([new_entry])
+#     new_entry_df.to_csv(LEADERBOARD_FILE, mode='a', index=False, header=False)
+
+# def load_leaderboard():
+#     """
+#     Load all submissions from the leaderboard file.
+#     """
+#     if not os.path.exists(LEADERBOARD_FILE) or os.stat(LEADERBOARD_FILE).st_size == 0:
+#         return pd.DataFrame({
+#             "Model Name": [],
+#             "Overall Accuracy": [],
+#             "Valid Accuracy": [],
+#             "Correct Predictions": [],
+#             "Total Questions": [],
+#             "Timestamp": [],
+#         })
+#     return pd.read_csv(LEADERBOARD_FILE)
+
+# def evaluate_predictions(prediction_file, model_name, add_to_leaderboard):
+#     """
+#     Evaluate predictions and optionally add results to the leaderboard.
+#     """
+#     ground_truth_file = "ground_truth.csv"
+#     if not os.path.exists(ground_truth_file):
+#         return "Ground truth file not found.", load_leaderboard()
+#     if not prediction_file:
+#         return "Prediction file not uploaded.", load_leaderboard()
+
+#     try:
+#         # Load predictions and ground truth
+#         predictions_df = pd.read_csv(prediction_file.name)
+#         ground_truth_df = pd.read_csv(ground_truth_file)
+
+#         # Merge predictions with ground truth
+#         merged_df = pd.merge(predictions_df, ground_truth_df, on='question_id', how='inner')
+#         merged_df['pred_answer'] = merged_df['predicted_answer'].apply(clean_answer)
+
+#         # Evaluate predictions
+#         valid_predictions = merged_df.dropna(subset=['pred_answer'])
+#         correct_predictions = (valid_predictions['pred_answer'] == valid_predictions['Answer']).sum()
+#         total_predictions = len(merged_df)
+#         total_valid_predictions = len(valid_predictions)
+
+#         # Calculate accuracy
+#         overall_accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0
+#         valid_accuracy = correct_predictions / total_valid_predictions if total_valid_predictions > 0 else 0
+
+#         results = {
+#             'model_name': model_name if model_name else "Unknown Model",
+#             'overall_accuracy': overall_accuracy,
+#             'valid_accuracy': valid_accuracy,
+#             'correct_predictions': correct_predictions,
+#             'total_questions': total_predictions,
+#         }
+
+#         # Update leaderboard only if opted in
+#         if add_to_leaderboard:
+#             update_leaderboard(results)
+#             return "Evaluation completed and added to leaderboard.", load_leaderboard()
+#         else:
+#             return "Evaluation completed but not added to leaderboard.", load_leaderboard()
+#     except Exception as e:
+#         return f"Error during evaluation: {str(e)}", load_leaderboard()
+
+# # Initialize leaderboard file
+# initialize_leaderboard_file()
+
+# # Gradio Interface
+# with gr.Blocks() as demo:
+#     gr.Markdown("# Prediction Evaluation Tool with Leaderboard")
+    
+#     with gr.Tabs():
+#         # Submission Tab
+#         with gr.TabItem("🏅 Submission"):
+#             file_input = gr.File(label="Upload Prediction CSV")
+#             model_name_input = gr.Textbox(label="Model Name", placeholder="Enter your model name")
+#             add_to_leaderboard_checkbox = gr.Checkbox(label="Add to Leaderboard?", value=True)
+#             eval_status = gr.Textbox(label="Evaluation Status", interactive=False)
+#             leaderboard_table_preview = gr.Dataframe(
+#                 value=load_leaderboard(),
+#                 label="Leaderboard (Preview)",
+#                 interactive=False,
+#                 wrap=True,
+#             )
+#             eval_button = gr.Button("Evaluate and Update Leaderboard")
+#             eval_button.click(
+#                 evaluate_predictions,
+#                 inputs=[file_input, model_name_input, add_to_leaderboard_checkbox],
+#                 outputs=[eval_status, leaderboard_table_preview],
+#             )
+        
+#         # Leaderboard Tab
+#         with gr.TabItem("🏅 Leaderboard"):
+#             leaderboard_table = gr.Dataframe(
+#                 value=load_leaderboard(),
+#                 label="Leaderboard",
+#                 interactive=False,
+#                 wrap=True,
+#             )
+#             refresh_button = gr.Button("Refresh Leaderboard")
+#             refresh_button.click(
+#                 lambda: load_leaderboard(),
+#                 inputs=[],
+#                 outputs=[leaderboard_table],
+#             )
+
+#     gr.Markdown(f"Last updated on **{LAST_UPDATED}**")
+
 # demo.launch()
+
+
 import gradio as gr
 import pandas as pd
-import os
 import re
 from datetime import datetime
+from huggingface_hub import hf_hub_download
+from datasets import Dataset
+import os
 
-LEADERBOARD_FILE = "leaderboard.csv"  # File to store all submissions persistently
+# Constants for Hugging Face repositories
+HF_TOKEN = os.getenv("HF_TOKEN")  # Hugging Face token stored as an environment variable
+LEADERBOARD_REPO = "SondosMB/leaderboard-dataset"  # Replace with your leaderboard dataset name
+GROUND_TRUTH_REPO = "SondosMB/ground-truth-dataset"  # Replace with your ground truth dataset name
 LAST_UPDATED = datetime.now().strftime("%B %d, %Y")
 
-def initialize_leaderboard_file():
+def load_ground_truth():
     """
-    Ensure the leaderboard file exists and has the correct headers.
+    Load the ground truth file from a private Hugging Face dataset.
     """
-    if not os.path.exists(LEADERBOARD_FILE):
-        # Create the file with headers
-        pd.DataFrame(columns=[
-            "Model Name", "Overall Accuracy", "Valid Accuracy",
-            "Correct Predictions", "Total Questions", "Timestamp"
-        ]).to_csv(LEADERBOARD_FILE, index=False)
-    else:
-        # Check if the file is empty and write headers if needed
-        if os.stat(LEADERBOARD_FILE).st_size == 0:
-            pd.DataFrame(columns=[
-                "Model Name", "Overall Accuracy", "Valid Accuracy",
-                "Correct Predictions", "Total Questions", "Timestamp"
-            ]).to_csv(LEADERBOARD_FILE, index=False)
+    try:
+        ground_truth_path = hf_hub_download(
+            repo_id=GROUND_TRUTH_REPO,
+            filename="ground_truth.csv",
+            use_auth_token=HF_TOKEN
+        )
+        return pd.read_csv(ground_truth_path)
+    except Exception as e:
+        print(f"Error loading ground truth: {e}")
+        return None
 
-def clean_answer(answer):
+def load_leaderboard():
     """
-    Clean and normalize the predicted answers.
+    Load the leaderboard from a private Hugging Face dataset.
     """
-    if pd.isna(answer):
-        return None
-    answer = str(answer)
-    clean = re.sub(r'[^A-Da-d]', '', answer)
-    if clean:
-        return clean[0].upper()
-    return None
+    try:
+        leaderboard_path = hf_hub_download(
+            repo_id=LEADERBOARD_REPO,
+            filename="leaderboard.csv",
+            use_auth_token=HF_TOKEN
+        )
+        return pd.read_csv(leaderboard_path)
+    except Exception as e:
+        print(f"Error loading leaderboard: {e}")
+        return pd.DataFrame({
+            "Model Name": [],
+            "Overall Accuracy": [],
+            "Valid Accuracy": [],
+            "Correct Predictions": [],
+            "Total Questions": [],
+            "Timestamp": [],
+        })
 
 def update_leaderboard(results):
     """
-    Append new submission results to the leaderboard file.
+    Append new submission results to the private leaderboard dataset.
     """
+    try:
+        # Load existing leaderboard or create a new one
+        leaderboard_path = hf_hub_download(
+            repo_id=LEADERBOARD_REPO,
+            filename="leaderboard.csv",
+            use_auth_token=HF_TOKEN
+        )
+        df = pd.read_csv(leaderboard_path)
+    except:
+        df = pd.DataFrame(columns=[
+            "Model Name", "Overall Accuracy", "Valid Accuracy",
+            "Correct Predictions", "Total Questions", "Timestamp"
+        ])
+
+    # Add new entry
     new_entry = {
         "Model Name": results['model_name'],
         "Overall Accuracy": round(results['overall_accuracy'] * 100, 2),
@@ -51,41 +241,38 @@ def update_leaderboard(results):
         "Total Questions": results['total_questions'],
         "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
     }
+    df = pd.concat([df, pd.DataFrame([new_entry])], ignore_index=True)
 
-    new_entry_df = pd.DataFrame([new_entry])
-    new_entry_df.to_csv(LEADERBOARD_FILE, mode='a', index=False, header=False)
+    # Save locally and push updated dataset to Hugging Face
+    df.to_csv("leaderboard.csv", index=False)
+    dataset = Dataset.from_pandas(df)
+    dataset.push_to_hub(LEADERBOARD_REPO, split="train", private=True)
 
-def load_leaderboard():
+def clean_answer(answer):
     """
-    Load all submissions from the leaderboard file.
+    Clean and normalize the predicted answers.
     """
-    if not os.path.exists(LEADERBOARD_FILE) or os.stat(LEADERBOARD_FILE).st_size == 0:
-        return pd.DataFrame({
-            "Model Name": [],
-            "Overall Accuracy": [],
-            "Valid Accuracy": [],
-            "Correct Predictions": [],
-            "Total Questions": [],
-            "Timestamp": [],
-        })
-    return pd.read_csv(LEADERBOARD_FILE)
+    if pd.isna(answer):
+        return None
+    answer = str(answer)
+    clean = re.sub(r'[^A-Da-d]', '', answer)
+    if clean:
+        return clean[0].upper()
+    return None
 
 def evaluate_predictions(prediction_file, model_name, add_to_leaderboard):
     """
     Evaluate predictions and optionally add results to the leaderboard.
     """
-    ground_truth_file = "ground_truth.csv"
-    if not os.path.exists(ground_truth_file):
+    ground_truth_df = load_ground_truth()
+    if ground_truth_df is None:
         return "Ground truth file not found.", load_leaderboard()
     if not prediction_file:
         return "Prediction file not uploaded.", load_leaderboard()
 
     try:
-        # Load predictions and ground truth
+        # Load predictions and merge with ground truth
         predictions_df = pd.read_csv(prediction_file.name)
-        ground_truth_df = pd.read_csv(ground_truth_file)
-
-        # Merge predictions with ground truth
         merged_df = pd.merge(predictions_df, ground_truth_df, on='question_id', how='inner')
         merged_df['pred_answer'] = merged_df['predicted_answer'].apply(clean_answer)
 
@@ -116,12 +303,9 @@ def evaluate_predictions(prediction_file, model_name, add_to_leaderboard):
     except Exception as e:
         return f"Error during evaluation: {str(e)}", load_leaderboard()
 
-# Initialize leaderboard file
-initialize_leaderboard_file()
-
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Prediction Evaluation Tool with Leaderboard")
+    gr.Markdown("# Secure Prediction Evaluation Tool with Private Leaderboard")
     
     with gr.Tabs():
         # Submission Tab
@@ -162,3 +346,4 @@ with gr.Blocks() as demo:
 
 demo.launch()
 
+