fix: Correct file paths for loading models in Hugging Face Space

frontend/app.py

@@ -57,13 +57,9 @@ st.title("Medi Scape Dashboard")
 # --- Session State Initialization ---
 if 'disease_model' not in st.session_state:
     try:
-        # Check if running on Hugging Face Spaces
-        if 'HF_SPACE_ID' in os.environ:
-            model_path = 'FINAL_MODEL.h5'  # Updated to .h5
-            st.session_state.disease_model = tf.keras.models.load_model(model_path)
-        else:  # Assume running locally
-            model_path = 'FINAL_MODEL.h5'  # Updated to .h5
-            st.session_state.disease_model = tf.keras.models.load_model(model_path)
+        # Assuming all models are in the root directory of your Hugging Face Space
+        model_path = 'FINAL_MODEL.h5'  
+        st.session_state.disease_model = tf.keras.models.load_model(model_path)
         print("Disease model loaded successfully!")
     except FileNotFoundError:
         st.error("Disease classification model not found. Please ensure 'FINAL_MODEL.h5' is in the correct directory.")
@@ -76,8 +72,6 @@ if 'disease_model' not in st.session_state:
 if 'vectorizer' not in st.session_state:
     try:
         vectorizer_path = "vectorizer.pkl"
-        print(f"Attempting to load vectorizer from: {vectorizer_path}")
-        print(f"Vectorizer file exists: {os.path.exists(vectorizer_path)}")
         st.session_state.vectorizer = pd.read_pickle(vectorizer_path)
         print("Vectorizer loaded successfully!")
     except FileNotFoundError:
@@ -90,14 +84,11 @@ if 'vectorizer' not in st.session_state:
 # Load the logistic regression model using joblib
 if 'model_llm' not in st.session_state:
     try:
-        llm_model_path = "logistic_regression_model.pkl"  # Corrected path
-        print(f"Attempting to load LLM model from: {llm_model_path}")
-        print(f"LLM Model file exists: {os.path.exists(llm_model_path)}")
-        # Load the model correctly using joblib
+        llm_model_path = "logistic_regression_model.pkl" 
         st.session_state.model_llm = joblib.load(llm_model_path)  
         print("LLM model loaded successfully!")
     except FileNotFoundError:
-        st.error("LLM model file not found. Please ensure 'logistic_regression_model.pkl' is in the 'frontend' directory.")
+        st.error("LLM model file not found. Please ensure 'logistic_regression_model.pkl' is in the correct directory.")
         st.session_state.model_llm = None
     except Exception as e:
         st.error(f"An error occurred while loading the LLM model: {e}")
@@ -107,13 +98,8 @@ if 'model_llm' not in st.session_state:
 
 # Load the disease classification model (outside session state for this example)
 try:
-    # Check if running on Hugging Face Spaces
-    if 'HF_SPACE_ID' in os.environ:
-        model_path = 'FINAL_MODEL.h5'  # Updated to .h5
-        disease_model = tf.keras.models.load_model(model_path)
-    else:  # Assume running locally
-        model_path = 'FINAL_MODEL.h5'  # Updated to .h5
-        disease_model = tf.keras.models.load_model(model_path)
+    model_path = 'FINAL_MODEL.h5'  
+    disease_model = tf.keras.models.load_model(model_path)
 except FileNotFoundError:
     st.error("Disease classification model not found. Please ensure 'FINAL_MODEL.h5' is in the correct directory.")
     disease_model = None