'functions'

Tools.py

@@ -5,9 +5,9 @@ import streamlit as st
 import pymysql
 import ast
 import re
-from utils import word_sentence_similarity, get_list_meaning_word, get_details_mantra_json, iast_process
+from utils import word_sentence_similarity, get_list_meaning_word, iast_process
 from llama_index.core.tools.tool_spec.base import BaseToolSpec
-from database import execute_query
+from database import execute_query, get_details_mantra_json
 import pandas as pd
 import json
 import ast
@@ -138,12 +138,12 @@ class MantraToolSpec(BaseToolSpec):
                 filter_df = self._get_mantra_details_by_scripture(scripture_name=scripture_name, KandahNumber=KandahNumber,MandalaNumber=MandalaNumber, ArchikahNumber=ArchikahNumber, 
                                                            ShuktaNumber=ShuktaNumber, PrapatakNumber=PrapatakNumber, MantraNumber=MantraNumber,
                                                            AnuvakNumber=AnuvakNumber, AdhyayaNumber=AdhyayaNumber)
-                mantra_id = filter_df['mantra_number'].values[0]
-                #print(mantra_id)
-                query = f"SELECT mantra_json FROM veda_content WHERE mantra_number = '{mantra_id}'"
+                if filter_df is not None:
+                    mantra_id = filter_df.iloc[0]['mantra_number']
+                    query = f"SELECT mantra_json FROM veda_content WHERE mantra_number = '{mantra_id}'"
             return self._get_mantra_details(query)
         except Exception as e:
-            return {"error": str(e)}
+            return json.dumps({"error": str(e)})
 
     def get_vedamantra_summary(self, mantraid=None, scripture_name=None, KandahNumber=None,MandalaNumber=None, ArchikahNumber=None, 
                                                            ShuktaNumber=None, PrapatakNumber=None, MantraNumber=None,
@@ -163,9 +163,12 @@ class MantraToolSpec(BaseToolSpec):
             else:
                 filtered_df = self._get_mantra_details_by_scripture(scripture_name=scripture_name, KandahNumber=KandahNumber,MandalaNumber=MandalaNumber, ArchikahNumber=ArchikahNumber, 
                                                            ShuktaNumber=ShuktaNumber, PrapatakNumber=PrapatakNumber, MantraNumber=MantraNumber,
-                                                           AnuvakNumber=AnuvakNumber, AdhyayaNumber=AdhyayaNumber)         
-                mantra_id = filtered_df['mantra_number'].values[0]
-                query = f"SELECT mantra_json FROM veda_content WHERE mantra_number = '{mantra_id}'"
+                                                           AnuvakNumber=AnuvakNumber, AdhyayaNumber=AdhyayaNumber)   
+                if filtered_df is not None:
+                    mantra_id = filtered_df.iloc[0]['mantra_number']
+                    query = f"SELECT mantra_json FROM veda_content WHERE mantra_number = '{mantra_id}'"
+                else:
+                    return None      
             json_dict = get_details_mantra_json(query)
             mantra_summary = json_dict['mantraSummary']['language']
             summary_dict = {"Roman-IAST summary of vedamantra": json_dict['mantraSummary']['language'][1]}
@@ -177,6 +180,7 @@ class MantraToolSpec(BaseToolSpec):
         except Exception as e:
             return {"error": str(e)}
 
+
 class PadaToolSpec(BaseToolSpec):
     '''
     Purpose: To obtains a complete or meaningful meaning of a word or pada based on context information.
@@ -198,15 +202,13 @@ class PadaToolSpec(BaseToolSpec):
         self.df_vedic_content = pd.read_csv(VEDAMANTRA_CSV_PATH,encoding = 'utf-8')
 
     def _get_pada_details_by_scripture(self, pada, scripture_name=None, KandahNumber=None,MandalaNumber=None, ArchikahNumber=None, 
-                                                           ShuktaNumber=None, PrapatakNumber=None, MantraNumber=None,
-                                                           AnuvakNumber=None, AdhyayaNumber=None):
+                                       ShuktaNumber=None, PrapatakNumber=None, MantraNumber=None,AnuvakNumber=None, AdhyayaNumber=None):
         try:
             pada = iast_process(pada)
             condition = (self.df_terms['Pada'] == pada)
 
             if scripture_name:
                 condition &= (self.df_terms['scripture_name'].str.lower() == scripture_name.lower())
-
             if KandahNumber:
                 condition &= (self.df_terms['KandahNumber'] == KandahNumber)
             if MandalaNumber:
@@ -256,10 +258,10 @@ class PadaToolSpec(BaseToolSpec):
                     if count >= best_count:
                         best_meaning = {cat: meanings.get(cat, None) for cat in ['adibhautic', 'adidaivic', 'adhyatmic']}
                         best_count = count
-            return best_meaning if best_meaning else {"error": "Required meaning associated with vedamantra is not available."}
+            return best_meaning if best_meaning else json.dumps({"error": "Required meaning associated with vedamantra is not available."})
         except Exception as e:
             logging.error(f"Error in _get_vedamantra_meaning: {e}")
-            return json.loads({"error": f"An error occurred: {e}"})
+            return json.dumps({"error": f"An error occurred: {e}"})
     
     def _get_pada_morphology(self, term_details, meanings):
         try:
@@ -282,7 +284,7 @@ class PadaToolSpec(BaseToolSpec):
             return []
 
     def get_pada_meaning(self, pada):
-        pada=iast_process(pada)
+        #pada=iast_process(pada)
         try:
             pada_details = self.df_terms[self.df_terms['Pada'] == pada]
             meanings_list = []
@@ -307,7 +309,7 @@ class PadaToolSpec(BaseToolSpec):
         2. What is the adhyatmic meaning of the pada agnim in the context of the mantra whose id is '1.1.1.1?'
         '''
         try:
-            pada = iast_process(pada)
+            #pada = iast_process(pada)
             if mantraid:
                 details = self.df_terms[(self.df_terms['mantra_id'] == mantraid) & (self.df_terms['Pada'] == pada)]
             else:
@@ -317,7 +319,7 @@ class PadaToolSpec(BaseToolSpec):
         
             if not details.empty:
                 pada_details = details.iloc[0]
-                #print(pada_details)
+                print(pada_details)
                 mantraID = pada_details['mantra_id']
                 meanings = self._get_vedamantra_meaning(mantraID,MahatmaName=MahatmaName)
                 if 'error' in meanings:
@@ -325,16 +327,16 @@ class PadaToolSpec(BaseToolSpec):
                 ab_term_morph_list = self._get_pada_morphology(pada_details, meanings['adibhautic'])
                 ad_term_morph_list = self._get_pada_morphology(pada_details, meanings['adidaivic'])
                 at_term_morph_list = self._get_pada_morphology(pada_details, meanings['adhyatmic'])
-                return {
+                return json.dumps({
                     f'adibhautic_info_{pada}': ab_term_morph_list,
                     'vedamantra_adibhautic_meaning': meanings['adibhautic'],
                     f'adidavic_info_{pada}': ad_term_morph_list,
                     'vedamantra_adidavic_meaning': meanings['adidaivic'],
                     f'adhyatmic_info_{pada}': at_term_morph_list,
                     'vedamantra_adhyatmic_meaning': meanings['adhyatmic']
-                }
+                })
             else:
                 return json.dumps({"error": f"No details found for pada '{pada}'"})
         except Exception as e:
             logging.error(f"Error in get_adibauatic_adidaivic_adhyatmic_meaning_of_pada: {e}")
-            return json.dumps({"error": f"Failed to get meaning of the word {pada}. {e}"})
+            return json.dumps({"error": f"Failed to get meaning of the word {pada}. {e}"})

app.py

@@ -154,7 +154,7 @@ context = """
   isn't adequate, I'll indicate the insufficiency. It's important to note that responses based on "Implicit" thought are not sourced 
   from the 'Svarupa Knowledge base', and I'll caution you accordingly.
 
-Additionally, I'll provide three follow-up questions tailored to the input query and context, aiming to deepen our exploration:
+  Additionally, I'll provide three follow-up questions tailored to the input query and context, aiming to deepen our exploration:
   *****
   You may also try the following questions: 
   1. Question1