Update app.py

app.py

@@ -116,6 +116,7 @@ def analyze_article(text: str) -> dict:
         "subjectivity_score": subjectivity_score,
         "reading_ease": reading_ease,
         "primary_tone": primary_tone,
+        "primary_theme": primary_theme,
         "tone_scores": tone_scores,
         "keywords": extracted_keywords,
         "entities": extracted_entities,
@@ -147,7 +148,7 @@ def _create_sentiment_gauge(score: float, title: str) -> go.Figure:
 
 def _create_comparison_radar_chart(results_a: dict, results_b: dict) -> go.Figure:
     """Generates an overlapping radar chart to compare emotions."""
-    categories = list(set(list(results_a["tone_scores"].keys()) + list(results_b["tone_scores"].keys())))
+    categories = sorted(list(set(list(results_a["tone_scores"].keys()) + list(results_b["tone_scores"].keys()))))
     
     val_a = [results_a["tone_scores"].get(c, 0) for c in categories]
     val_b = [results_b["tone_scores"].get(c, 0) for c in categories]
@@ -224,7 +225,7 @@ def check_contradiction(text_a: str, text_b: str) -> dict:
     premise = text_a[:400]
     hypothesis = text_b[:400]
     
-    nli_input = f"{premise} </s></s> {hypothesis}"
+    nli_input = {"text": premise, "text_pair": hypothesis}
     result = models["nli"](nli_input)[0]
     
     return {"relationship": result["label"], "confidence": result["score"]}
@@ -283,7 +284,7 @@ if st.session_state.results_a and st.session_state.results_b:
     # Display Contradictions
     nli_result = st.session_state.nli_result
     if nli_result:
-        if nli_result["relationship"] == "CONTRADICTION":
+        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":
             st.success(f"**NARRATIVE ALIGNMENT** (Confidence: {nli_result['confidence']:.2f}) - These sources agree on the core premise.")