Disable generation controls for static mode

attribution_analysis/attribution_analysis_page.py

@@ -546,7 +546,20 @@ def verify_claims(claims, analysis_data):
                     score_series = analysis_data['scores_df'].abs().mean(axis=1)
                     score_name = "average score"
                 else: # peak
-                    score_series = analysis_data['scores_df'].abs().max(axis=1)
+                    # Check both influence GIVEN (input) and RECEIVED (output)
+                    # We use fillna(0) to handle cases where a token is not in that axis
+                    input_peaks = analysis_data['scores_df'].abs().max(axis=1)
+                    output_peaks = analysis_data['scores_df'].abs().max(axis=0)
+                    
+                    combined_scores = {}
+                    all_tokens = set(input_peaks.index) | set(output_peaks.index)
+                    
+                    for t in all_tokens:
+                        s1 = input_peaks.get(t, 0.0)
+                        s2 = output_peaks.get(t, 0.0)
+                        combined_scores[t] = max(s1, s2)
+                    
+                    score_series = pd.Series(combined_scores)
                     score_name = "peak score"
 
                 if score_series.empty:
@@ -879,10 +892,9 @@ def run_analysis(prompt, max_tokens, enable_explanations, force_exact_num_tokens
             cached_result = cached_data[prompt]
             
             # Check if influential_docs are missing and update the cache if possible
-            # FORCE UPDATE FOR DEBUGGING
-            if True: # "influential_docs" not in cached_result:
+            if "influential_docs" not in cached_result:
                 try:
-                    print(f"DEBUG: Force updating cache for '{prompt}'...")
+                    print(f"Updating cache for '{prompt}' with missing influence docs...")
                     lang = st.session_state.get('lang', 'en')
                     # This call should hit the Streamlit cache and be fast
                     missing_docs = get_influential_docs(prompt, lang)
@@ -890,12 +902,9 @@ def run_analysis(prompt, max_tokens, enable_explanations, force_exact_num_tokens
                     if missing_docs:
                         cached_result["influential_docs"] = missing_docs
                         # Save updated cache back to file
-                        print(f"DEBUG: Writing to {cache_file}...")
                         with open(cache_file, "w", encoding="utf-8") as f:
                             json.dump(cached_data, f, ensure_ascii=False, indent=4)
                         print("Cache updated successfully.")
-                    else:
-                        print("DEBUG: No docs returned from get_influential_docs")
                 except Exception as e:
                     print(f"Could not update cache with influence docs: {e}")
 
@@ -1103,7 +1112,8 @@ def show_attribution_analysis():
             min_value=1,
             max_value=50,
             value=5,
-            help=tr('max_new_tokens_slider_help')
+            help=tr('max_new_tokens_slider_help'),
+            disabled=True
         )
         
         # Checkbox to enable or disable AI explanations.
@@ -1118,7 +1128,8 @@ def show_attribution_analysis():
             tr('generate_and_analyze_button'), 
             type="primary",
             on_click=start_new_analysis,
-            args=(prompt, max_tokens, enable_explanations)
+            args=(prompt, max_tokens, enable_explanations),
+            disabled=True
         )
     
     with col2:

function_vectors/function_vectors_page.py

@@ -303,7 +303,7 @@ def display_interactive_analysis():
     enable_ai_explanation = st.checkbox(tr('enable_ai_explanation_checkbox'), value=True, help=tr('enable_ai_explanation_help'))
 
     # Analysis button.
-    if st.button(tr('analyze_button'), type="primary"):
+    if st.button(tr('analyze_button'), type="primary", disabled=True):
         _trigger_and_rerun_analysis(input_text, True, True, enable_ai_explanation)