Update app_1.py

app_1.py

@@ -8,6 +8,20 @@ import tqdm
 from label_studio_sdk.client import Client
 from getpass import getpass
 
+from huggingface_hub import login 
+
+login(token = os.getenv('HF_TOKEN'))
+
+model_id = "RTLucassen/flan-t5-large-finetuned-melanocytic-lesion-reports"
+
+local_dir = "models"
+
+hf_hub_download(
+    repo_id=repo_id,
+    filename=model_id,
+    local_dir = local_dir
+)
+
 class ModelPredictor:
     def __init__(self, model_name, file_to_predict_on, data_dir = "./data/preprocessed_data/", model_dir="./models/"):
         """
@@ -138,14 +152,7 @@ model_path = BASE_DIR / "models"
 models = [model.name for model in model_path.iterdir() if model.is_dir()]
 
 for model_name in models:
-    # Predict on the validation set example
-    predictor = ModelPredictor(model_name, "validation")
-    predictor.predict()
-
-    # Predict on the training set example
-    predictor = ModelPredictor(model_name, "training")
-    predictor.predict()
-
+    
     # Predict on the unlabeled set example
     predictor = ModelPredictor(model_name, "unlabeled")
     predictor.predict()
@@ -333,4 +340,22 @@ def import_annotations_existing_tasks(predictions: List[Dict], label_studio_proj
             print(f"Importing annotations to project {label_studio_project_id}")
             print(project.create_predictions(prediction_for_this_project))
     
-    return "Annotations imported successfully."
+    return "Annotations imported successfully."
+
+def segment_report(file):
+    
+    return ''
+
+    
+input_files = gr.File()
+
+iface = gr.Interface(
+    fn=segment_report,
+    inputs=input_files,
+    outputs=['text'],
+    title='Segment Reports',
+    description="This application helps segmenting medical report into meaningful fragments",
+    theme=gr.themes.Soft(),
+)
+
+iface.launch()