From 0770f07b974bf68cef60a4235fa3fd2ca148586c Mon Sep 17 00:00:00 2001 From: vikassingh767844-bit Date: Sat, 2 May 2026 10:39:32 +0530 Subject: [PATCH] Create ticket processing script in main.py Added a script to process support tickets from a CSV file and save the results. --- code/main.py | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/code/main.py b/code/main.py index e69de29b..b5c4ad50 100644 --- a/code/main.py +++ b/code/main.py @@ -0,0 +1,51 @@ +import pandas as pd +import os + +# 1. Paths set karein +TICKETS_PATH = '../support_tickets/support_tickets.csv' +OUTPUT_PATH = '../support_tickets/output.csv' +DATA_DIR = '../data/' + +def process_tickets(): + # CSV load karein + if not os.path.exists(TICKETS_PATH): + print("Error: support_tickets.csv nahi mili!") + return + + df = pd.read_csv(TICKETS_PATH) + + results = [] + + for index, row in df.iterrows(): + ticket_id = row['ticket_id'] + customer_query = row['customer_query'] + + print(f"Processing ticket {ticket_id}...") + + # --- AI LOGIC YAHAN AAYEGI --- + # Aapko yahan RAG (Retrieval-Augmented Generation) use karna hoga + # jo data/ folder se sahi file uthaye. + + status = "replied" # ya "escalated" + product_area = "General" + response = "Ye aapka dummy response hai. AI integration baaki hai." + justification = "Query was simple and found in docs." + request_type = "product_issue" # product_issue, bug, etc. + # ----------------------------- + + results.append({ + "ticket_id": ticket_id, + "status": status, + "product_area": product_area, + "response": response, + "justification": justification, + "request_type": request_type + }) + + # Results ko output.csv mein save karein + output_df = pd.DataFrame(results) + output_df.to_csv(OUTPUT_PATH, index=False) + print(f"Kaam ho gaya! Output save ho gaya hai: {OUTPUT_PATH}") + +if __name__ == "__main__": + process_tickets()