import google.generativeai as genai
import pandas as pd
import os
import json
from data import df_all_cards

#handling intent classification for retrieval
def handle_query_classification(user_query):
    genai.configure(api_key=os.environ.get("api_key_1"))  
    model1 = genai.GenerativeModel('gemini-2.0-flash')
    prompt = f"""
        You are a smart financial assistant.
        
        ### User's Query:
        {user_query}
        
        ### Task:
        Classify the user's intent into one of the following categories:
        1. "retrieve" → If the user is asking for card suggestions, recommendations, or showing cards (e.g., "suggest a card", "need a travel card") OR if they mention their lifestyle, income, spending, or needs (e.g., travel, shopping, fuel, rewards, luxury).
        2. "specific" → If the user is asking about a particular credit card by name (even if the word "card" is not used). Examples: "Tell me about HDFC Regalia", "Is SBI Elite good?".
        3. "no_retrieval" → ONLY if the query is generic (e.g., “What is credit score?”), casual chit-chat (e.g., “Hi”), or doesn’t mention any lifestyle, financial needs, or specific card names.

        
        Respond ONLY in the following JSON format:
        If intent is "no_retrieval", you MUST include a helpful 'response' field.
        If intent is "retrieve" or "specific", do NOT include any response or explanation.
        
        Respond in this exact format:
        {{
          "intent": "retrieve" | "specific" | "no_retrieval",
          "response": "Only include this if intent is 'no_retrieval'"
        }}
        
        """

    raw_response = model1.generate_content(prompt).text.strip()

    # Clean any markdown formatting if present
    if raw_response.startswith("```"):
        raw_response = raw_response.strip("`").strip()
        if raw_response.startswith("json"):
            raw_response = raw_response[len("json"):].strip()

    try:
        parsed = json.loads(raw_response)
        return parsed
    except Exception as e:
        print("JSON parsing error:", e)
        print("Raw response from LLM:", raw_response)
        raise
# result = handle_query_classification("Want to optimize my spending – travel often, premium hotels, and online shopping.")
# if result["intent"] == "no_retrieval":
#     print(result['response'])

#passing the card mentioned in the user query
def find_matching_card(user_query):
    lowered_query = user_query.lower()
    for _, row in df_all_cards.iterrows():
        if row["name"].lower() in lowered_query:
            return row.to_dict()
    return None


#for queries enquiring about a card
def generate_card_response_with_context(user_query, card_info):
    genai.configure(api_key=os.environ.get("api_key_1"))  
    model1 = genai.GenerativeModel('gemini-2.0-flash')
    prompt = f"""
You are a helpful financial assistant. A user has asked about a specific credit card.

Card Name: {card_info.get('name')}
Description: {card_info.get('description')}

User's Question: {user_query}

Please provide a concise, relevant answer using the above card context.
"""
    response = model1.generate_content(prompt)
    return response.text.strip()