Added logging with the logging package

app.py

@@ -15,6 +15,9 @@ from sentence_transformers import SentenceTransformer
 from peft import PeftModel
 from bs4 import BeautifulSoup
 import requests
+import logging
+
+logging.basicConfig(format='[%(asctime)s] %(message)s', datefmt='%d-%b-%y %H:%M:%S', level=logging.INFO)
 
 headers = {
     "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_5) AppleWebKit 537.36 (KHTML, like Gecko) Chrome",
@@ -24,7 +27,7 @@ headers = {
 
 
 def google_search(text):
-    print(f"Google search on: {text}")
+    logging.info(f"Google search on: {text}")
     try:
         site = requests.get(f"https://www.google.com/search?hl=en&q={text}", headers=headers)
         main = (
@@ -40,10 +43,10 @@ def google_search(text):
 
         ans = "  \n".join(res)
     except Exception as ex:
-        print(f"Error: {ex}")
+        logging.error(f"Error: {ex}")
         ans = ""
 
-    print(f"The result of the google search is: {ans}")
+    logging.info(f"The result of the google search is: {ans}")
 
     return ans
 
@@ -112,13 +115,13 @@ def get_results_from_pinecone(query, top_k=3, re_rank=True, verbose=True):
         return []
 
     if verbose:
-        print("Query:", query)
+        logging.info("Query:", query)
 
     final_results = []
 
     if re_rank:
         if verbose:
-            print("Document ID (Hash)\t\tRetrieval Score\tCE Score\tText")
+            logging.info("Document ID (Hash)\t\tRetrieval Score\tCE Score\tText")
 
         sentence_combinations = [
             [query, result_from_pinecone["metadata"]["text"]] for result_from_pinecone in results_from_pinecone
@@ -135,17 +138,17 @@ def get_results_from_pinecone(query, top_k=3, re_rank=True, verbose=True):
             result_from_pinecone = results_from_pinecone[idx]
             final_results.append(result_from_pinecone)
             if verbose:
-                print(
+                logging.info(
                     f"{result_from_pinecone['id']}\t{result_from_pinecone['score']:.2f}\t{similarity_scores[idx]:.2f}\t{result_from_pinecone['metadata']['text'][:50]}"
                 )
         return final_results
 
     if verbose:
-        print("Document ID (Hash)\t\tRetrieval Score\tText")
+        logging.info("Document ID (Hash)\t\tRetrieval Score\tText")
     for result_from_pinecone in results_from_pinecone:
         final_results.append(result_from_pinecone)
         if verbose:
-            print(
+            logging.info(
                 f"{result_from_pinecone['id']}\t{result_from_pinecone['score']:.2f}\t{result_from_pinecone['metadata']['text'][:50]}"
             )
 
@@ -268,13 +271,13 @@ def text_to_text_generation(verbose, prompt):
         match response_num:
             case 0:
                 prompt = f"[INST] {prompt}\n Lets think step by step. [/INST] {start_template}"
-                print('Kubectl command prompt:')
-                print(prompt)
+                logging.info('Kubectl command prompt:')
+                logging.info(prompt)
             case 1:
                 if retriever == "semantic_search":
                     question = prompt
-                    print('Semantic search prompt:')
-                    print(
+                    logging.info('Semantic search prompt:')
+                    logging.info(
                         (
                         f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: [RETRIEVED_RESULTS_FROM_BOOK] [INST] Answer the following question: {question} [/INST]\nAnswer:  \n")
 
@@ -295,8 +298,8 @@ def text_to_text_generation(verbose, prompt):
                     question = prompt
                     prompt = f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: {retrieved_results} </s>\n<s> [INST] Answer the following question: {prompt} [/INST]\nAnswer: "
                     
-                    print('Google search prompt:')
-                    print(
+                    logging.info('Google search prompt:')
+                    logging.info(
                         (
                             f"You are a helpful kubernetes professional. [INST] Use the following documentation, if it is relevant to answer the question below. [/INST]\nDocumentation: [RETRIEVED_RESULTS_FROM_GOOGLE] [INST] Answer the following question: {question} [/INST]\nAnswer:\n\n" 
                         )
@@ -312,13 +315,13 @@ def text_to_text_generation(verbose, prompt):
                     )
                 else:
                     prompt = f"[INST] Answer the following question: {prompt} [/INST]\nAnswer: "
-                    print('No retriever question prompt:')
-                    print(prompt)
+                    logging.info('No retriever question prompt:')
+                    logging.info(prompt)
 
             case _:
                 prompt = f"[INST] {prompt} [/INST]"
-                print('Other question prompt:')
-                print(prompt)
+                logging.info('Other question prompt:')
+                logging.info(prompt)
 
         return prompt, md
 
@@ -350,8 +353,8 @@ def text_to_text_generation(verbose, prompt):
 
     modes = ["Kubectl command", "Kubernetes related", "Other"]
 
-    print(f'{" Query Start ":-^40}')
-    print("Classified as: " + modes[response_num])
+    logging.info(f'{" Query Start ":-^40}')
+    logging.info("Classified as: " + modes[response_num])
 
     modes[response_num] = f"**{modes[response_num]}**"
     modes = " / ".join(modes)
@@ -419,16 +422,16 @@ def text_to_text_generation(verbose, prompt):
         res_prompt, res_semantic_search_prompt, res_google_search_prompt
     )
 
-    print("SEMANTIC BEFORE CLEANUP: ", gen_semantic_search)
-    print("GOOGLE BEFORE CLEANUP: ", gen_google_search)
+    logging.info("SEMANTIC BEFORE CLEANUP: ", gen_semantic_search)
+    logging.info("GOOGLE BEFORE CLEANUP: ", gen_google_search)
 
 
     res_prompt, res_normal = cleanup(*gen_normal)
     res_semantic_search_prompt, res_semantic_search = cleanup(*gen_semantic_search)
     res_google_search_prompt, res_google_search = cleanup(*gen_google_search)
 
-    print("SEMANTIC AFTER CLEANUP: ", res_semantic_search)
-    print("GOOGLE AFTER CLEANUP: ", res_google_search)
+    logging.info("SEMANTIC AFTER CLEANUP: ", res_semantic_search)
+    logging.info("GOOGLE AFTER CLEANUP: ", res_google_search)
 
     if verbose:
         return (