Update app.py

app.py

@@ -1,204 +1,241 @@
+import torch
+import time
+import numpy as np
+import pandas as pd
+import evaluate
 import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from sklearn.metrics import accuracy_score, classification_report
 from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
-import pandas as pd
-from apscheduler.schedulers.background import BackgroundScheduler
-from huggingface_hub import snapshot_download
-
-from src.about import (
-    CITATION_BUTTON_LABEL,
-    CITATION_BUTTON_TEXT,
-    EVALUATION_QUEUE_TEXT,
-    INTRODUCTION_TEXT,
-    LLM_BENCHMARKS_TEXT,
-    TITLE,
-)
-from src.display.css_html_js import custom_css
-from src.display.utils import (
-    BENCHMARK_COLS,
-    COLS,
-    EVAL_COLS,
-    EVAL_TYPES,
-    AutoEvalColumn,
-    ModelType,
-    fields,
-    WeightType,
-    Precision
-)
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
-from src.populate import get_evaluation_queue_df, get_leaderboard_df
-from src.submission.submit import add_new_eval
-
-
-def restart_space():
-    API.restart_space(repo_id=REPO_ID)
-
-### Space initialisation
-try:
-    print(EVAL_REQUESTS_PATH)
-    snapshot_download(
-        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
-    )
-except Exception:
-    restart_space()
-try:
-    print(EVAL_RESULTS_PATH)
-    snapshot_download(
-        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
-    )
-except Exception:
-    restart_space()
-
-
-LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-
-(
-    finished_eval_queue_df,
-    running_eval_queue_df,
-    pending_eval_queue_df,
-) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
-
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+# Load Accuracy and F1-Score Metrics
+accuracy_metric = evaluate.load("accuracy")
+f1_metric = evaluate.load("f1")
+
+# Define Model Paths
+MODEL_PATHS = {
+    "MindBERT": "DrSyedFaizan/mindBERT",
+    "BERT-base": "bert-base-uncased",
+    "RoBERTa": "roberta-base",
+    "DistilBERT": "distilbert-base-uncased"
+}
+
+# Load Test Dataset (Example: Reddit Mental Health)
+test_texts = [
+    "I feel so anxious and panicked all the time.",
+    "I'm feeling absolutely wonderful today!",
+    "I don't think I can go on anymore, I feel suicidal.",
+    "Lately, I have mood swings that I can't explain.",
+    "I feel so stressed out about everything."
+]
+test_labels = [0, 3, 6, 1, 5]  # Anxiety, Normal, Suicidal, Bipolar, Stress
+
+# Define column structure for leaderboard
+@dataclass
+class ModelEvalColumn:
+    name: str
+    type: str
+    displayed_by_default: bool = True
+    never_hidden: bool = False
+    hidden: bool = False
+
+# Define the columns for your leaderboard
+fields = lambda cls: [
+    ModelEvalColumn(name="model", type="str", never_hidden=True),
+    ModelEvalColumn(name="model_type", type="str"),
+    ModelEvalColumn(name="precision", type="str"),
+    ModelEvalColumn(name="params", type="number"),
+    ModelEvalColumn(name="accuracy", type="number"),
+    ModelEvalColumn(name="f1_score", type="number"),
+    ModelEvalColumn(name="inference_time", type="number"),
+    ModelEvalColumn(name="license", type="str", displayed_by_default=False),
+]
+
+# Function to evaluate models and format for leaderboard
+def evaluate_models():
+    results = []
+    
+    # Model metadata (you would normally get this from model card or API)
+    model_metadata = {
+        "MindBERT": {"model_type": "BERT", "precision": "float16", "params": 0.11, "license": "MIT"},
+        "BERT-base": {"model_type": "BERT", "precision": "float16", "params": 0.11, "license": "Apache-2.0"},
+        "RoBERTa": {"model_type": "RoBERTa", "precision": "float16", "params": 0.125, "license": "MIT"},
+        "DistilBERT": {"model_type": "DistilBERT", "precision": "float16", "params": 0.067, "license": "Apache-2.0"}
+    }
+    
+    for model_name, model_path in MODEL_PATHS.items():
+        print(f"Evaluating {model_name}...")
+        # Load Tokenizer and Model
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        model = AutoModelForSequenceClassification.from_pretrained(model_path)
+        model.eval()
+        
+        # Tokenize Test Data
+        inputs = tokenizer(test_texts, padding=True, truncation=True, return_tensors="pt")
+        
+        # Measure Inference Time
+        start_time = time.time()
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+            predictions = torch.argmax(logits, dim=1).numpy()
+        end_time = time.time()
+        
+        # Compute Metrics
+        accuracy = accuracy_score(test_labels, predictions)
+        f1_score = f1_metric.compute(predictions=predictions, references=test_labels, average="macro")["f1"]
+        inference_time = round(end_time - start_time, 4)
+        
+        # Store Results with additional metadata needed for leaderboard
+        result = {
+            "model": model_name,
+            "model_type": model_metadata[model_name]["model_type"],
+            "precision": model_metadata[model_name]["precision"],
+            "params": model_metadata[model_name]["params"], 
+            "accuracy": round(accuracy, 4),
+            "f1_score": round(f1_score, 4),
+            "inference_time": inference_time,
+            "license": model_metadata[model_name]["license"]
+        }
+        results.append(result)
+    
+    # Convert to DataFrame
+    df_results = pd.DataFrame(results)
+    return df_results
+
+# Initialize leaderboard with custom columns
 def init_leaderboard(dataframe):
     if dataframe is None or dataframe.empty:
         raise ValueError("Leaderboard DataFrame is empty or None.")
+    
+    columns = fields(ModelEvalColumn)
+    
     return Leaderboard(
         value=dataframe,
-        datatype=[c.type for c in fields(AutoEvalColumn)],
+        datatype=[c.type for c in columns],
         select_columns=SelectColumns(
-            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
-            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
+            default_selection=[c.name for c in columns if c.displayed_by_default],
+            cant_deselect=[c.name for c in columns if c.never_hidden],
             label="Select Columns to Display:",
         ),
-        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
-        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
+        search_columns=["model", "license"],
+        hide_columns=[c.name for c in columns if c.hidden],
         filter_columns=[
-            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
-            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
+            ColumnFilter("model_type", type="checkboxgroup", label="Model types"),
+            ColumnFilter("precision", type="checkboxgroup", label="Precision"),
             ColumnFilter(
-                AutoEvalColumn.params.name,
+                "params",
                 type="slider",
                 min=0.01,
-                max=150,
+                max=0.5,
                 label="Select the number of parameters (B)",
             ),
-            ColumnFilter(
-                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
-            ),
         ],
-        bool_checkboxgroup_label="Hide models",
         interactive=False,
     )
 
-
+# Custom CSS similar to the original
+custom_css = """
+.markdown-text {
+    padding: 0 20px;
+}
+.tab-buttons button.selected {
+    background-color: #FF9C00 !important;
+    color: white !important;
+}
+"""
+
+# Create Gradio Interface
 demo = gr.Blocks(css=custom_css)
-with demo:
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
 
+with demo:
+    gr.HTML("<h1>Mental Health Model Evaluation Benchmark</h1>")
+    gr.Markdown("This benchmark evaluates various transformer models on mental health classification tasks.", elem_classes="markdown-text")
+    
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
-            leaderboard = init_leaderboard(LEADERBOARD_DF)
-
-        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
-            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
-
-        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
-            with gr.Column():
-                with gr.Row():
-                    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,
-                    ):
-                        with gr.Row():
-                            finished_eval_table = gr.components.Dataframe(
-                                value=finished_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-                    with gr.Accordion(
-                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            running_eval_table = gr.components.Dataframe(
-                                value=running_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-
-                    with gr.Accordion(
-                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            pending_eval_table = gr.components.Dataframe(
-                                value=pending_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-            with gr.Row():
-                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
-
+        with gr.TabItem("🏅 Model Benchmark", elem_id="model-benchmark-tab", id=0):
+            # Get evaluation results
+            df_results = evaluate_models()
+            leaderboard = init_leaderboard(df_results)
+        
+        with gr.TabItem("📝 About", elem_id="about-tab", id=1):
+            gr.Markdown("""
+            ## About This Benchmark
+            
+            This leaderboard compares various transformer models on mental health text classification tasks.
+            The benchmark uses a test set from Reddit Mental Health datasets with examples covering anxiety, 
+            depression, bipolar disorder, suicidal ideation, stress, and normal emotional states.
+            
+            Models are evaluated on:
+            - Accuracy
+            - F1-Score (Macro)
+            - Inference Time
+            
+            ### Model Types
+            - BERT-based models
+            - RoBERTa models
+            - DistilBERT models
+            - Specialized mental health models (MindBERT)
+            """, elem_classes="markdown-text")
+            
+        with gr.TabItem("🚀 Submit Model", elem_id="submit-tab", id=2):
+            gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+            
             with gr.Row():
                 with gr.Column():
                     model_name_textbox = gr.Textbox(label="Model name")
-                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+                    model_path_textbox = gr.Textbox(label="Model path (HF repo ID)")
                     model_type = gr.Dropdown(
-                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
+                        choices=["BERT", "RoBERTa", "DistilBERT", "GPT", "T5", "Other"],
                         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],
+                        choices=["float16", "float32", "int8", "int4"],
                         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")
+                    params = gr.Number(label="Parameters (billions)", value=0.11)
+                    license = gr.Textbox(label="License", value="Apache-2.0")
+                    
+            submit_button = gr.Button("Submit Model for Evaluation")
             submission_result = gr.Markdown()
+            
+            # This would typically connect to a submission system
+            def handle_submission(model_name, model_path, model_type, precision, params, license):
+                return f"Model {model_name} successfully submitted for evaluation. It will appear in the leaderboard once processing is complete."
+            
             submit_button.click(
-                add_new_eval,
-                [
-                    model_name_textbox,
-                    base_model_name_textbox,
-                    revision_name_textbox,
-                    precision,
-                    weight_type,
-                    model_type,
-                ],
+                handle_submission,
+                [model_name_textbox, model_path_textbox, model_type, precision, params, license],
                 submission_result,
             )
 
     with gr.Row():
         with gr.Accordion("📙 Citation", open=False):
+            citation_text = """
+            @misc{mental-health-model-benchmark,
+              author = {Syed Faizan},
+              title = {Mental Health Model Benchmark},
+              year = {2025},
+              publisher = {GitHub},
+              url = {https://github.com/SYEDFAIZAN1987/mindBERT}
+            }
+            """
             citation_button = gr.Textbox(
-                value=CITATION_BUTTON_TEXT,
-                label=CITATION_BUTTON_LABEL,
-                lines=20,
+                value=citation_text,
+                label="Citation",
+                lines=10,
                 elem_id="citation-button",
                 show_copy_button=True,
             )
 
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=1800)
-scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+demo.launch()