Update src/peri/value_chains.py

src/peri/value_chains.py

@@ -61,9 +61,9 @@ def analyze_budget_trend(data:pd.DataFrame):
 
     # 3. Apply numpy.select with a default fallback value
     df["trend"] = np.select(conditions, choices, default=np.nan)
-    return df
+    return df, conditions
 
-def get_scores(data:pd.DataFrame):
+def get_scores(data:pd.DataFrame, conditions:List):
     df = data.copy()
     results = df[["value_chain", "trend"]].reset_index(drop=True)
 
@@ -79,9 +79,9 @@ def get_scores(data:pd.DataFrame):
     results["strategic_importance_score"] = (0.8*df[strat_imp_cols].sum(axis=1))+(0.2*results["breadth"]).reset_index(drop=True)
     results["institutional_committment_score"] = df[[c for c in df.columns if "value" in c and "institutional_commitment" in c]].sum(axis=1)
 
-    return normalize_committment_scores(results)
+    return normalize_committment_scores(results, conditions)
 
-def normalize_committment_scores(results:pd.DataFrame):
+def normalize_committment_scores(results:pd.DataFrame, conditions:List):
     
     results["trend_normalized"] = (results["trend"]-min(1, results["trend"].min()))/max(len(conditions), results["trend"].max())
     results["strategic_importance_normalized"] = results["strategic_importance_score"]/3.4
@@ -97,5 +97,5 @@ def analyze_vc_committment(llm_outputs:Dict):
     c2vc_df = c2vc_df.replace("NOT_FOUND", 0)
     c2vc_df = c2vc_df.replace("NO", 0)
 
-    trend = analyze_budget_trend(c2vc_df)
-    return get_scores(trend)
+    trend, conditions = analyze_budget_trend(c2vc_df)
+    return get_scores(trend, conditions)