Update app.py

app.py

@@ -10,8 +10,15 @@ import io
 nltk.download('punkt')
 
 # Load AI models once to optimize performance
-tone_model = pipeline("zero-shot-classification", model="facebook/roberta-large-mnli")
-frame_model = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+try:
+    tone_model = pipeline("zero-shot-classification", model="cross-encoder/nli-deberta-v3-large")
+except OSError:
+    st.error("Failed to load tone analysis model. Please check internet connection or model availability.")
+
+try:
+    frame_model = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+except OSError:
+    st.error("Failed to load frame classification model. Please check internet connection or model availability.")
 
 # Updated tone categories
 tone_categories = [
@@ -33,15 +40,23 @@ def detect_language(text):
     except Exception:
         return "unknown"
 
-# Analyze tone using RoBERTa model
+# Analyze tone using DeBERTa model
 def analyze_tone(text):
-    model_result = tone_model(text, candidate_labels=tone_categories)
-    return model_result["labels"][:2]  # Top 2 tone labels
+    try:
+        model_result = tone_model(text, candidate_labels=tone_categories)
+        return model_result["labels"][:2]  # Top 2 tone labels
+    except Exception as e:
+        st.error(f"Error analyzing tone: {e}")
+        return ["Unknown"]
 
 # Extract frames using BART model
 def extract_frames(text):
-    model_result = frame_model(text, candidate_labels=frame_categories)
-    return model_result["labels"][:2]  # Top 2 frame labels
+    try:
+        model_result = frame_model(text, candidate_labels=frame_categories)
+        return model_result["labels"][:2]  # Top 2 frame labels
+    except Exception as e:
+        st.error(f"Error extracting frames: {e}")
+        return ["Unknown"]
 
 # Extract hashtags
 def extract_hashtags(text):