Update main.py

main.py

@@ -16,6 +16,7 @@ from langchain.prompts import (
 )
 import pytz
 import uvicorn
+import json
 
 # Load environment variables
 load_dotenv()
@@ -24,7 +25,7 @@ if not OPENAI_API_KEY:
     raise ValueError("OPENAI_API_KEY is not set.")
 
 # Initialize FastAPI
-app = FastAPI(title="PMO360 Prompt-Based Weekly Reporter", version="5.1")
+app = FastAPI(title="PMO360 Prompt-Based Weekly Reporter", version="5.2")
 
 # Connect to DB
 print("Connecting to SQL Server...")
@@ -131,17 +132,16 @@ name_extractor = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, openai_api_key
 name_prompt = PromptTemplate(
     input_variables=["text"],
     template="""
-You will extract the name and optional date range from the prompt for generating a weekly update.
+Extract user's first name, last name, and date range from the given prompt.
 
 Examples:
-- "Show Anirban Chatterjee's weekly update" → {"first_name": "Anirban", "last_name": "Chatterjee"}
-- "Weekly update for Aditya from April 1 to April 7" → {"first_name": "Aditya", "start_date": "2025-04-01", "end_date": "2025-04-07"}
-- "DPR for Rahul Kumar between 15 April and 20 April" → {"first_name": "Rahul", "last_name": "Kumar", "start_date": "2025-04-15", "end_date": "2025-04-20"}
+"Weekly report for Anirban Chatterjee from April 15 to April 20"
+→ {"first_name": "Anirban", "last_name": "Chatterjee", "start_date": "2025-04-15", "end_date": "2025-04-20"}
 
-Respond strictly in JSON with:
-- first_name (required)
-- last_name (optional)
-- start_date and end_date in YYYY-MM-DD format (optional)
+"DPR for Rahul this week"
+→ {"first_name": "Rahul"}
+
+Respond ONLY with a valid Python dictionary, no explanation or quotes around it.
 
 Prompt: {text}
 """
@@ -155,11 +155,12 @@ class PromptRequest(BaseModel):
 @app.post("/weekly-dpr")
 async def get_prompt_based_dpr(request: PromptRequest):
     try:
-        from json import loads
         prompt_text = request.prompt.strip()
         name_response = name_extractor.invoke(name_prompt.format_prompt(text=prompt_text).to_string())
         print("LLM raw output:", name_response.content)
-        name_json = loads(name_response.content.strip().strip("'").strip('"'))
+
+        # Safely parse AI-native dictionary
+        name_json = eval(name_response.content)
 
         first = name_json.get("first_name")
         last = name_json.get("last_name")