Update app.py

app.py

@@ -43,9 +43,10 @@ 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.
-    "primary_theme": Choose ONE from: ["economic consequences", "moral and ethical fairness", "legal and bureaucratic", "public safety and health"].
+    "dynamic_topic": A 3-to-5 word summary of the article's specific angle or frame.
+    "key_entities": A dictionary of the top 3 entities (people, groups, concepts) mentioned, and a 1-to-2 word description of how they are portrayed (e.g., {{"Tech Leaders": "Victims", "Government": "Aggressor"}}).
     "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 the 5 to 8 most emotionally charged, biased, or subjective words used in the text (e.g., ["draconian", "slammed", "titans", "catastrophic"]).
+    "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).
     
     Text to analyze:
@@ -75,9 +76,10 @@ def analyze_article(text: str) -> dict:
     return {
         "sentiment_score": llm_data.get("sentiment_score", 0.0),
         "primary_tone": llm_data.get("primary_tone", "neutral"),
-        "primary_theme": llm_data.get("primary_theme", "unclear"),
+        "dynamic_topic": llm_data.get("dynamic_topic", "Unclear Topic"), 
+        "key_entities": llm_data.get("key_entities", {}), 
         "tone_scores": standard_tones, 
-        "framing_words": llm_data.get("framing_words", []),
+        "framing_words": llm_data.get("framing_words", []), 
         "subjectivity_score": llm_data.get("subjectivity_score", 0.0), 
         "reading_ease": max(0.0, min(100.0, raw_reading_ease)),
     }
@@ -147,17 +149,24 @@ def _create_comparison_radar_chart(results_a: dict, results_b: dict) -> go.Figur
 
 
 def _highlight_framing_words(text: str, target_words: list) -> str:
-    """Highlights LLM-identified framing words in the synced text snippet."""
+    """Highlights LLM-identified framing words and tags their specific emotion."""
     display_text = _truncate_to_words(text, MAX_WORDS)
     if not display_text:
         return ""
     
     highlighted_text = display_text + ("..." if len(text.split()) > MAX_WORDS else "")
     
-    for word in target_words:
+    for item in target_words:
+        if isinstance(item, dict):
+            word = item.get("word", "")
+            emotion = str(item.get("emotion", "charged")).upper()
+        else:
+            word = str(item)
+            emotion = "CHARGED"
+
         if len(word) > 2: 
             pattern = r'\b(' + re.escape(word) + r')\b'
-            replacement = r"<span style='background-color: #fef08a; color: #854d0e; font-weight: 600; padding: 0.1rem 0.2rem; border-radius: 4px;'>\1</span>"
+            replacement = rf"<span style='background-color: #fef08a; color: #854d0e; font-weight: 600; padding: 0.1rem 0.2rem; border-radius: 4px;'>\1 <span style='font-size: 0.65em; background: #854d0e; color: white; padding: 2px 4px; border-radius: 3px; margin-left: 2px; vertical-align: middle;'>{emotion}</span></span>"
             highlighted_text = re.sub(pattern, replacement, highlighted_text, flags=re.IGNORECASE)
 
     return highlighted_text
@@ -365,11 +374,17 @@ 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("Framing Lens", str(r_a['primary_theme']).title())
+        m3.metric("Dynamic Topic", str(r_a['dynamic_topic']).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")
 
+        st.markdown("**Entities Portrayed:**")
+        entities = r_a.get('key_entities', {})
+        if entities:
+            entities_html = "".join([f"<span style='background:#f1f5f9; border: 1px solid #cbd5e1; padding:4px 8px; border-radius:6px; margin-right:8px; font-size:0.85em; display: inline-block; margin-bottom: 4px;'><b>{k}</b>: <i>{v}</i></span>" for k, v in entities.items()])
+            st.markdown(f"<div style='margin-bottom: 1rem;'>{entities_html}</div>", unsafe_allow_html=True)
+
         st.markdown("**Key Framing Language:**")
         annotated_text = _highlight_framing_words(user_article_a, r_a['framing_words'])
         st.markdown(f"<div style='background-color: #f8fafc; padding: 1rem; border-radius: 8px; border: 1px solid #e2e8f0;'>{annotated_text}</div>", unsafe_allow_html=True)
@@ -382,10 +397,16 @@ 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("Framing Lens", str(r_b['primary_theme']).title())
+        m3.metric("Dynamic Topic", str(r_b['dynamic_topic']).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")
+        st.plotly_chart(_create_sentiment_gauge(r_b["sentiment_score"], "Sentiment Bias"), use_container_width=True, key="gauge_a")
+
+        st.markdown("**Entities Portrayed:**")
+        entities = r_b.get('key_entities', {})
+        if entities:
+            entities_html = "".join([f"<span style='background:#f1f5f9; border: 1px solid #cbd5e1; padding:4px 8px; border-radius:6px; margin-right:8px; font-size:0.85em; display: inline-block; margin-bottom: 4px;'><b>{k}</b>: <i>{v}</i></span>" for k, v in entities.items()])
+            st.markdown(f"<div style='margin-bottom: 1rem;'>{entities_html}</div>", unsafe_allow_html=True)
 
         st.markdown("**Key Framing Language:**")
         annotated_text = _highlight_framing_words(user_article_b, r_b['framing_words'])