Added logging and replaced all print statements with logger

Author: ParagGhatage

backend/app/db/vector_store.py

@@ -22,6 +22,10 @@
 
 import os
 from pinecone import Pinecone, ServerlessSpec, CloudProvider, AwsRegion
+from app.logging.logging_config import setup_logger
+
+
+logger = setup_logger(__name__)
 
 # Load Pinecone credentials from environment variables
 PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
@@ -41,15 +45,15 @@
 
 # Create index if it doesn't exist
 if not pc.has_index(INDEX_NAME):
-    print(f"Creating index: {INDEX_NAME}")
+    logger.info(f"Creating index: {INDEX_NAME}")
     pc.create_index(
         name=INDEX_NAME,
         dimension=DIMENSIONS,
         metric=METRIC,
         spec=ServerlessSpec(cloud=CloudProvider.AWS, region=AwsRegion.US_EAST_1),
     )
 else:
-    print(f"Index '{INDEX_NAME}' already exists")
+    logger.info(f"Index '{INDEX_NAME}' already exists")
 
 try:
     # Connect to the index

backend/app/logging/logging_config.py

@@ -0,0 +1,39 @@
+import logging
+import sys
+
+def setup_logger(name: str) -> logging.Logger:
+    """
+    Creates and configures a logger with console + file output.
+
+    Args:
+        name (str): The logger's name (usually __name__ of the calling module).
+
+    Returns:
+        logging.Logger: Configured logger instance.
+    """
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)  # Log everything from DEBUG and above
+
+    # Avoid adding duplicate handlers if logger already set
+    if logger.handlers:
+        return logger
+
+    # Formatter with timestamp, log level, module name
+    formatter = logging.Formatter(
+        "[%(asctime)s] [%(levelname)s] [%(name)s]: %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S"
+    )
+
+    # Console Handler
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(logging.INFO)
+    console_handler.setFormatter(formatter)
+    logger.addHandler(console_handler)
+
+    # File Handler
+    file_handler = logging.FileHandler("app.log")
+    file_handler.setLevel(logging.DEBUG)  # Keep detailed logs in file
+    file_handler.setFormatter(formatter)
+    logger.addHandler(file_handler)
+
+    return logger

backend/app/modules/bias_detection/check_bias.py

@@ -26,6 +26,9 @@
 from groq import Groq
 from dotenv import load_dotenv
 import json
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
 
 load_dotenv()
 
@@ -34,10 +37,11 @@
 
 def check_bias(text):
     try:
-        print(text)
-        print(json.dumps(text))
+        logger.debug(f"Raw article text: {text}")
+        logger.debug(f"JSON dump of text: {json.dumps(text)}")
 
         if not text:
+            logger.error("Missing or empty 'cleaned_text'")
             raise ValueError("Missing or empty 'cleaned_text'")
 
         chat_completion = client.chat.completions.create(
@@ -61,16 +65,16 @@ def check_bias(text):
             temperature=0.3,
             max_tokens=512,
         )
-
         bias_score = chat_completion.choices[0].message.content.strip()
+        logger.info(f"Bias score calculated: {bias_score}")
 
         return {
             "bias_score": bias_score,
             "status": "success",
         }
 
     except Exception as e:
-        print(f"Error in bias_detection: {e}")
+        logger.exception("Error in bias detection")
         return {
             "status": "error",
             "error_from": "bias_detection",

backend/app/modules/chat/llm_processing.py

@@ -26,6 +26,9 @@
 import os
 from groq import Groq
 from dotenv import load_dotenv
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
 
 load_dotenv()
 
@@ -41,7 +44,7 @@ def build_context(docs):
 
 def ask_llm(question, docs):
     context = build_context(docs)
-    print(context)
+    logger.debug(f"Generated context for LLM:\n{context}")
     prompt = f"""You are an assistant that answers based on context.
 
 Context:
@@ -58,5 +61,5 @@ def ask_llm(question, docs):
             {"role": "user", "content": prompt},
         ],
     )
-
+    logger.info("LLM response retrieved successfully.")
     return response.choices[0].message.content

backend/app/modules/facts_check/llm_processing.py

@@ -28,6 +28,9 @@
 from dotenv import load_dotenv
 import json
 import re
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
 
 load_dotenv()
 
@@ -66,6 +69,8 @@ def run_claim_extractor_sdk(state):
         )
 
         extracted_claims = chat_completion.choices[0].message.content.strip()
+        logger.debug(f"Extracted claims:\n{extracted_claims}")
+
 
         return {
             **state,
@@ -74,7 +79,7 @@ def run_claim_extractor_sdk(state):
         }
 
     except Exception as e:
-        print(f"Error in claim_extraction: {e}")
+        logger.exception("Error in claim_extraction")
         return {
             "status": "error",
             "error_from": "claim_extraction",
@@ -132,13 +137,13 @@ def run_fact_verifier_sdk(search_results):
 
             # Strip markdown code blocks if present
             content = re.sub(r"^```json|```$", "", content).strip()
-            print(content)
+            logger.debug(f"Raw LLM fact verification output:\n{content}")
 
             # Try parsing the JSON response
             try:
                 parsed = json.loads(content)
             except Exception as parse_err:
-                print(f"❌ LLM JSON parse error: {parse_err}")
+                logger.error(f"LLM JSON parse error: {parse_err}")
 
             results_list.append(parsed)
 
@@ -149,7 +154,7 @@ def run_fact_verifier_sdk(search_results):
         }
 
     except Exception as e:
-        print(f"🔥 Error in fact_verification: {e}")
+        logger.exception("Error in fact_verification")
         return {
             "status": "error",
             "error_from": "fact_verification",

backend/app/modules/langgraph_nodes/error_handler.py

@@ -13,10 +13,15 @@
 """
 
 
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
+
+
 def error_handler(input):
-    print("Error detected!")
-    print(f"From: {input.get('error_from')}")
-    print(f"Message: {input.get('message')}")
+    error_from = input.get("error_from", "Unknown source")
+    message = input.get("message", "No message provided")
+    logger.error(f"Error detected from '{error_from}': {message}")
 
     return {
         "status": "stopped_due_to_error",

backend/app/modules/langgraph_nodes/fact_check.py

@@ -16,6 +16,10 @@
 
 
 from app.utils.fact_check_utils import run_fact_check_pipeline
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
+
 
 
 def run_fact_check(state):
@@ -28,15 +32,15 @@ def run_fact_check(state):
         verifications, error_message = run_fact_check_pipeline(state)
 
         if error_message:
-            print(f"some error occured in fact_checking:{error_message}")
+            logger.error(f"Error in fact-checking: {error_message}")
             return {
                 "status": "error",
                 "error_from": "fact_checking",
                 "message": f"{error_message}",
             }
 
     except Exception as e:
-        print(f"some error occured in fact_checking:{e}")
+        logger.exception(f"Unexpected error in fact-checking: {e}")
         return {
             "status": "error",
             "error_from": "fact_checking",

backend/app/modules/langgraph_nodes/generate_perspective.py

@@ -23,6 +23,9 @@
 from app.utils.prompt_templates import generation_prompt
 from langchain_groq import ChatGroq
 from pydantic import BaseModel, Field
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
 
 
 prompt = generation_prompt
@@ -73,7 +76,7 @@ def generate_perspective(state):
             }
         )
     except Exception as e:
-        print(f"some error occured in generate_perspective:{e}")
+        logger.exception(f"Error in generate_perspective: {e}")
         return {
             "status": "error",
             "error_from": "generate_perspective",

backend/app/modules/langgraph_nodes/judge.py

@@ -18,6 +18,9 @@
 import re
 from langchain_groq import ChatGroq
 from langchain.schema import HumanMessage
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
 
 # Init once
 groq_llm = ChatGroq(
@@ -62,7 +65,7 @@ def judge_perspective(state):
         return {**state, "score": score, "status": "success"}
 
     except Exception as e:
-        print(f"Error in judge_perspective: {e}")
+        logger.exception(f"Error in judge_perspective: {e}")
         return {
             "status": "error",
             "error_from": "judge_perspective",

backend/app/modules/langgraph_nodes/sentiment.py

@@ -17,6 +17,9 @@
 import os
 from groq import Groq
 from dotenv import load_dotenv
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
 
 load_dotenv()
 
@@ -61,7 +64,7 @@ def run_sentiment_sdk(state):
         }
 
     except Exception as e:
-        print(f"Error in sentiment_analysis: {e}")
+        logger.exception(f"Error in sentiment_analysis: {e}")
         return {
             "status": "error",
             "error_from": "sentiment_analysis",