Update app.py

app.py

@@ -9,10 +9,7 @@ import io
 import base64
 import os
 import huggingface_hub
-from huggingface_hub import hf_hub_download
-from huggingface_hub import login
-
-
+from huggingface_hub import hf_hub_download, login
 
 # Load label mapping
 label_to_int = pd.read_pickle('label_to_int.pkl')
@@ -52,11 +49,6 @@ def load_model():
     input_size = 768 + metadata_features
     log_reg = LogisticRegressionTorch(input_dim=input_size, output_dim=N_UNIQUE_CLASSES)
 
-    #model_weights_path = os.getenv('MODEL_PATH')
-    #weights = torch.load(model_weights_path, map_location=torch.device('cpu'))
-    
-
-    # Ensure you are authenticated if the repository is private
     token = os.getenv('HUGGINGFACE_TOKEN')
     if token is None:
         raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")
@@ -79,7 +71,12 @@ def load_model():
 
 model, tokenizer = load_model()
 
-def analyze_dna(sequence):
+def analyze_dna(sequence, password):
+
+    # put password into hidden HF variable later
+    if password != "test_pw":
+        return {"error": "Invalid password"}, ""
+
     try:
         if not all(nucleotide in 'ACTGN' for nucleotide in sequence):
             return {"error": "Sequence contains invalid characters"}, ""
@@ -114,7 +111,11 @@ def analyze_dna(sequence):
         return {"error": str(e)}, ""
 
 # Create a Gradio interface
-demo = gr.Interface(fn=analyze_dna, inputs="text", outputs=["json", "html"])
+demo = gr.Interface(
+    fn=analyze_dna,
+    inputs=[gr.inputs.Textbox(label="DNA Sequence"), gr.inputs.Textbox(label="Password", type="password")],
+    outputs=["json", "html"]
+)
 
 # Launch the interface
 demo.launch()