Update app.py

app.py

@@ -15,7 +15,9 @@ import io
 import streamlit.components.v1 as components
 import functools
 import threading
-from typing import Dict, Tuple, List, Any, Optional
+from typing import Dict, Tuple, List, Any, Optional, Union
+from concurrent.futures import ThreadPoolExecutor
+import numpy as np
 
 # Suppress warnings for a clean console
 logging.getLogger("torch").setLevel(logging.CRITICAL)
@@ -23,6 +25,14 @@ logging.getLogger("transformers").setLevel(logging.CRITICAL)
 warnings.filterwarnings("ignore")
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
+# Check if NumPy is available
+try:
+    test_array = np.array([1, 2, 3])
+    torch.from_numpy(test_array)
+except Exception as e:
+    st.error(f"NumPy is not available or incompatible with PyTorch: {str(e)}. Ensure 'numpy' is in requirements.txt and reinstall dependencies.")
+    st.stop()
+
 # Check if CUDA is available, otherwise use CPU
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"Using device: {device}")
@@ -744,7 +754,7 @@ def display_analysis_results(transcribed_text):
         else:
             st.write("No emotions detected.")
 
-    # Expert analysis section (new feature while maintaining UI)
+    # Expert analysis section
     with st.expander("Expert Analysis", expanded=False):
         col1, col2 = st.columns(2)
         
@@ -791,7 +801,7 @@ def display_analysis_results(transcribed_text):
             elif sentiment == "POSITIVE" and not is_sarcastic:
                 st.markdown("👍 **Clear Positive Expression:** The content expresses genuine positive sentiment without sarcasm.")
 
-    # Original debug expander (maintained from original code)
+    # Debug expander
     with st.expander("Debug Information", expanded=False):
         st.write("Debugging information for troubleshooting:")
         for i, debug_line in enumerate(st.session_state.debug_info[-10:]):
@@ -802,7 +812,7 @@ def display_analysis_results(transcribed_text):
                 if score > 0.01:  # Only show non-negligible scores
                     st.text(f"{emotion}: {score:.4f}")
 
-    # Original analysis details expander (maintained from original code)
+    # Analysis details expander
     with st.expander("Analysis Details", expanded=False):
         st.write("""
         *How this works:*