Create ticket processing script in main.py

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()