Update app.py

app.py

@@ -48,7 +48,7 @@ def analyze_article(text: str) -> dict:
     Keys to return:
     "sentiment_score": A float between -1.0 (highly negative) and 1.0 (highly positive).
     "primary_tone": The single dominant emotion.
-    "dynamic_topic": A 3-to-5 word summary of the article's specific angle or frame.
+    "primary_theme": Choose ONE like: ["economic consequences", "moral and ethical fairness", "legal and bureaucratic", "public safety and health"].
     "tone_scores": A dictionary scoring THESE EXACT 6 EMOTIONS from 0.0 to 1.0: {{"anger": 0.0, "fear": 0.0, "joy": 0.0, "sadness": 0.0, "surprise": 0.0, "trust": 0.0}}.
     "framing_words": A list of dictionaries containing the 5 to 8 most emotionally charged or biased words, and the specific emotion they evoke. Format: [{{"word": "draconian", "emotion": "fear"}}, {{"word": "titans", "emotion": "awe"}}].
     "subjectivity_score": A float between 0.0 (completely objective/factual) and 1.0 (highly opinionated/subjective).
@@ -86,7 +86,7 @@ def analyze_article(text: str) -> dict:
     return {
         "sentiment_score": llm_data.get("sentiment_score", 0.0),
         "primary_tone": llm_data.get("primary_tone", "neutral"),
-        "dynamic_topic": llm_data.get("dynamic_topic", "Unclear Topic"), 
+        "primary_theme": llm_data.get("primary_theme", "unclear"),
         "tone_scores": standard_tones, 
         "framing_words": llm_data.get("framing_words", []), 
         "subjectivity_score": llm_data.get("subjectivity_score", 0.0), 
@@ -395,7 +395,7 @@ if st.session_state.results_a and st.session_state.results_b:
         m3, m4 = st.columns(2)
         m1.metric("Subjectivity", f"{r_a['subjectivity_score']:.2f}", help="0 is objective, 1 is highly opinionated.")
         m2.metric("Primary Emotion", str(r_a['primary_tone']).title())
-        m3.metric("Dynamic Topic", str(r_a['dynamic_topic']).title())
+        m3.metric("Framing Lens", str(r_a['primary_theme']).title())
         m4.metric("Reading Ease", f"{r_a['reading_ease']:.1f}", help="0-30 is college graduate level, 60-70 is 8th grade.")
 
         st.plotly_chart(_create_sentiment_gauge(r_a["sentiment_score"], "Sentiment Bias"), use_container_width=True, key="gauge_a")
@@ -412,7 +412,7 @@ if st.session_state.results_a and st.session_state.results_b:
         m3, m4 = st.columns(2)
         m1.metric("Subjectivity", f"{r_b['subjectivity_score']:.2f}", help="0 is objective, 1 is highly opinionated.")
         m2.metric("Primary Emotion", str(r_b['primary_tone']).title())
-        m3.metric("Dynamic Topic", str(r_b['dynamic_topic']).title()) 
+        m3.metric("Framing Lens", str(r_b['primary_theme']).title())
         m4.metric("Reading Ease", f"{r_b['reading_ease']:.1f}", help="0-30 is college graduate level, 60-70 is 8th grade.")
 
         st.plotly_chart(_create_sentiment_gauge(r_b["sentiment_score"], "Sentiment Bias"), use_container_width=True, key="gauge_b")