Adding support to get the Feedback on Answer shared by KCC-FTAs in Custom Query widget

app.py

@@ -162,6 +162,21 @@ class DomState:
         return self.kkms_kssw_obj.weather_utils_obj.get_weather_forecast(state, district)
 
 
+    def click_handler_for_feedback(
+        self,
+        question_category,
+        question,
+        answer,
+        feedback
+    ):
+        self.kkms_kssw_obj.langchain_utils_obj.save_answer_feeback(
+            question_category,
+            question,
+            answer,
+            feedback
+        )
+
+
     def click_handler_for_get_kb_sources(
         self
     ):
@@ -386,6 +401,25 @@ with gr.Blocks(title='KKMS-Smart-Search-Demo') as demo:
                         )
                     b_indic_lang_answer = gr.Button("Get answer in selected language").style(size='sm')
                     b_indic_lang_answer.click(fn=dom.click_handler_for_get_indic_translation, inputs=[answer, language], outputs=[indic_lang_answer])
+
+        with gr.Column(scale=1, min_width=600):
+            with gr.Tab(label='Feedback'):
+                # Submit feedback for the answer
+                feedback = gr.Radio(
+                    [
+                        "Correct",
+                        "Incorrect",
+                        "Partially Correct and contains irrelevant text",
+                        "Correct but not complete",
+                    ],
+                    label="Answer is",
+                    value="Correct"
+                )
+                b_feedback = gr.Button("Submit Feedback").style(size='sm')
+                b_feedback.click(
+                    fn=dom.click_handler_for_feedback,
+                    inputs=[question_category, question, answer, feedback]
+                )
     
     
     #############################################################################

requirements.txt

@@ -19,4 +19,4 @@ googletrans==3.1.0a0
 BeautifulSoup4
 pypdf
 PyPDF2
-html2text
+html2text

src/constants.py

@@ -3,18 +3,28 @@ import src.web_crawler as web_crawler_utils
 import src.weather as weather_utils
 
 # Wheater to load the existing index store or create from scratch?
-LOAD_FROM_EXISTING_INDEX_STORE = False
+LOAD_FROM_EXISTING_INDEX_STORE = True
 INDEX_TYPE = 'FAISS'
 
 # Path from where to load the data (from the local directory)
 DATA_PATH = './data/'
 
+OUTPUT_DIR = './output/'
+if not os.path.exists(OUTPUT_DIR):
+    os.makedirs(OUTPUT_DIR)
 # Path to store the index/vector db
-OUTPUT_PATH = os.path.join('./output/', INDEX_TYPE)
+OUTPUT_PATH = os.path.join(OUTPUT_DIR, INDEX_TYPE)
 # Create OUTPUT_PATH directory if not present
 if not os.path.exists(OUTPUT_PATH):
     os.makedirs(OUTPUT_PATH)
 
+# Output path to store the feedback for the answers received by KCCs-FTAs
+OUTPUT_PATH_ANSWER_FEEDBACK = os.path.join(OUTPUT_DIR, 'answers_feedback')
+if not os.path.exists(OUTPUT_PATH_ANSWER_FEEDBACK):
+    os.makedirs(OUTPUT_PATH_ANSWER_FEEDBACK)
+OUTPUT_PATH_ANSWER_FEEDBACK_FILE_PREFIX = 'answers_feedback'
+OUTPUT_PATH_ANSWER_FEEDBACK_FILE_SAVE_SEPARATOR = '\t'
+
 # Index categories (There would be an index for each category. On asking the query, App will search for the relevant docs/information only from the respective index category.)
 INDEX_CATEGORY = [
     'crops',

src/data_loader.py

@@ -14,10 +14,10 @@ from langchain.docstore.document import Document
 import src.utils as utils
 
 import logging
-logger = logging.getLogger(__name__)
 logging.basicConfig(
     format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
 )
+logger = logging.getLogger(__name__)
 
 import warnings
 warnings.filterwarnings('ignore')

src/kkms_kssw.py

@@ -8,10 +8,10 @@ import src.translator as translator_utils
 import src.web_crawler as web_crawler_utils
 
 import logging
-logger = logging.getLogger(__name__)
 logging.basicConfig(
     format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
 )
+logger = logging.getLogger(__name__)
 
 import warnings
 warnings.filterwarnings('ignore')

src/langchain_utils.py

@@ -19,15 +19,18 @@ from langchain.vectorstores import FAISS
 import pickle
 import shutil
 from typing import Dict, List, Optional
-
+import pandas as pd
+from datetime import datetime
 import os
 os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')
 
 import logging
-logger = logging.getLogger(__name__)
 logging.basicConfig(
-    format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
+    format="%(asctime)s %(levelname)s [%(name)s] %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S"
 )
+logger = logging.getLogger(__name__)
 
 import warnings
 warnings.filterwarnings('ignore')
@@ -910,3 +913,38 @@ class LANGCHAIN_UTILS:
                             self.index_category_doc_type_wise_data_sources[index_category][dt].add(val.metadata['source'])
         
         return self.index_category_doc_type_wise_data_sources
+
+
+    def save_answer_feeback(
+        self,
+        question_category,
+        question,
+        answer,
+        feedback
+    ):
+        logger.info(f'Question category: {question_category}')
+        logger.info(f'Question: {question}')
+        logger.info(f'Answer: {answer}')
+        logger.info(f'Answer feedback is: {feedback}')
+
+        feedback_filepath = os.path.join(
+            constants_utils.OUTPUT_PATH_ANSWER_FEEDBACK,
+            f'{constants_utils.OUTPUT_PATH_ANSWER_FEEDBACK_FILE_PREFIX}_{question_category}.tsv'
+        )
+
+        if os.path.exists(feedback_filepath):
+            df = pd.read_csv(feedback_filepath, sep=constants_utils.OUTPUT_PATH_ANSWER_FEEDBACK_FILE_SAVE_SEPARATOR)
+        else:
+            df = pd.DataFrame(columns=['question_category', 'question', 'answer', 'feedback', 'timestamp'])
+
+        # Append answer feedback to df
+        df.loc[len(df)] = {
+            'question_category': question_category,
+            'question': question,
+            'answer': answer,
+            'feedback': feedback,
+            'timestamp': datetime.strftime(datetime.now(), '%Y-%m-%d %H:%M:%S')
+        }
+
+        # Save df into TSV format
+        df.to_csv(feedback_filepath, sep=constants_utils.OUTPUT_PATH_ANSWER_FEEDBACK_FILE_SAVE_SEPARATOR, index=False, header=True)

src/utils.py

@@ -4,10 +4,10 @@ import pandas as pd
 from urllib.parse import urlparse
 
 import logging
-logger = logging.getLogger(__name__)
 logging.basicConfig(
     format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
 )
+logger = logging.getLogger(__name__)
 
 
 class UTILS: