Update app.py

app.py

@@ -14,6 +14,7 @@ if not HUGGINGFACE_TOKEN:
 # Function to load model and tokenizer (local or Hugging Face with token)
 def load_model(model_path):
     try:
+        # Load tokenizer and model with authentication token if required
         tokenizer = AutoTokenizer.from_pretrained(model_path, use_auth_token=HUGGINGFACE_TOKEN)
         model = AutoModelForSeq2SeqLM.from_pretrained(model_path, use_auth_token=HUGGINGFACE_TOKEN)
         return tokenizer, model
@@ -22,15 +23,17 @@ def load_model(model_path):
         return None, None
 
 # Set device (use GPU if available)
-device = "cuda" if torch.cuda.is_available() else "cpu"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Path to your model (either a local path or a Hugging Face model name)
 model_path = "Izza-shahzad-13/fine-tuned-flan-t5"  # Use your Hugging Face model identifier
 
 # Load tokenizer and model
 tokenizer, model = load_model(model_path)
-if model:
+if model and tokenizer:
     model.to(device)
+else:
+    st.stop()  # Stop the app if model or tokenizer failed to load
 
 # Function to generate response from the model
 def generate_response(input_text):
@@ -55,9 +58,12 @@ st.title("FLAN-T5 Mental Health Counseling Assistant")
 st.write("Type your thoughts or feelings, and let the model respond.")
 
 # User input for interaction
-user_input = st.text_input("How are you feeling today?")
+user_input = st.text_area("How are you feeling today?", placeholder="Type here...")
 
 # Generate and display model response when input is provided
-if user_input:
-    response = generate_response(user_input)
+if user_input.strip():  # Check if input is not empty
+    with st.spinner("Generating response..."):
+        response = generate_response(user_input)
     st.write("Model Response:", response)
+else:
+    st.info("Please enter your thoughts or feelings in the text area above.")