download model and load inside evalaute

app.py

@@ -4,15 +4,19 @@ import pandas as pd
 from typing import List, Dict
 from flow_judge import Hf, FlowJudge, EvalInput
 from flow_judge.metrics import CustomMetric, RubricItem
+from huggingface_hub import snapshot_download
+from flow_judge.models.huggingface import Hf
 
+MODEL_NAME = "flowaicom/Flow-Judge-v0.1"
 
-@spaces.GPU
-def load_model():
+def download_model():
     try:
-        model = Hf(flash_attn=False)
-        return model
-    except Exception as e:  
-        raise RuntimeError(f"Failed to initialize Hf Model: {e}")
+        print(f"Downloading model {MODEL_NAME}...")
+        snapshot_download(repo_id=MODEL_NAME)
+        print(f"Model {MODEL_NAME} downloaded to default Hugging Face cache")
+        return True
+    except Exception as e:
+        raise RuntimeError(f"Failed to download model {MODEL_NAME}: {e}")
 
 EXAMPLES = [
     {
@@ -40,7 +44,13 @@ def populate_fields(example_index: int):
     )
     
 @spaces.GPU
-def evaluate(model, task_inputs: pd.DataFrame, task_output: pd.DataFrame, evaluation_criteria: str, rubric: pd.DataFrame) -> tuple:
+def evaluate(task_inputs: pd.DataFrame, task_output: pd.DataFrame, evaluation_criteria: str, rubric: pd.DataFrame) -> tuple:
+    # Load the model
+    try:
+        model = Hf(flash_attn=False)
+    except Exception as e:
+        raise RuntimeError(f"Failed to initialize Hf Model: {e}")
+
     # Convert inputs to the expected format
     eval_input = EvalInput(
         inputs=[{row['Name']: row['Value']} for _, row in task_inputs.iterrows()],
@@ -100,7 +110,7 @@ def reset_evaluation_criteria():
     )
 
 with gr.Blocks() as demo:
-    model = load_model()
+    model_downloaded = download_model()
     with gr.Row():
         example_buttons = [gr.Button(f"{example['emoji']} Example {i+1}") for i, example in enumerate(EXAMPLES)]
 
@@ -184,7 +194,7 @@ with gr.Blocks() as demo:
 
     evaluate_btn.click(
         evaluate,
-        inputs=[model,task_inputs, task_output, evaluation_criteria, rubric],
+        inputs=[task_inputs, task_output, evaluation_criteria, rubric],
         outputs=[feedback, score]
     )