Update app.py

app.py

@@ -74,8 +74,12 @@ def analyze_article(text: str) -> dict:
         sentiment_score = 0.0  # Neutral
 
     # Emotion Classification
-    emotion_results = models["emotion"](safe_text)[0]
-    if isinstance(emotion_results, list):
+    emotion_results = models["emotion"](safe_text)
+    
+    if isinstance(emotion_results, list) and isinstance(emotion_results[0], list):
+        emotion_results = emotion_results[0]
+        
+    if isinstance(emotion_results, list) and len(emotion_results) > 0:
         tone_scores = {res["label"]: res["score"] for res in emotion_results}
         primary_tone = emotion_results[0]["label"]
     else:
@@ -224,10 +228,13 @@ def check_contradiction(text_a: str, text_b: str) -> dict:
     models = _load_nlp_models()
     premise = text_a[:400]
     hypothesis = text_b[:400]
-    
+
     nli_input = {"text": premise, "text_pair": hypothesis}
-    result = models["nli"](nli_input)[0]
+    result = models["nli"](nli_input)
     
+    if isinstance(result, list):
+        result = result[0]
+
     return {"relationship": result["label"], "confidence": result["score"]}
     
 
@@ -286,7 +293,7 @@ if st.session_state.results_a and st.session_state.results_b:
     if nli_result:
         if nli_result["relationship"].upper() == "CONTRADICTION":
             st.error(f"**NARRATIVE CONTRADICTION** (Confidence: {nli_result['confidence']:.2f}) - These sources are disputing each other's facts.")
-        elif nli_result["relationship"] == "ENTAILMENT":
+        elif nli_result["relationship"].upper() == "ENTAILMENT":
             st.success(f"**NARRATIVE ALIGNMENT** (Confidence: {nli_result['confidence']:.2f}) - These sources agree on the core premise.")
         else:
             st.info(f"**NEUTRAL RELATIONSHIP** - These sources are discussing the topic without direct contradiction or alignment.")