import os
import json
import requests
from sentence_transformers import SentenceTransformer, util
from dotenv import load_dotenv


# ✅ Load .env file
load_dotenv()

# ✅ Hugging Face API Token check
HF_API_TOKEN = os.getenv("HF_API_TOKEN")

if not HF_API_TOKEN:
    raise ValueError("Error: Hugging Face API Token is missing! Please check your .env file.")

# ✅ Hugging Face GPT Model API Endpoint
GPT_MODEL_API = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"

# ✅ Headers for API request
headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}

# ✅ Load sentence transformer model for intent matching
similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# ✅ Load dataset
with open("ds.json") as f:
    dataset = json.load(f)

# ✅ Precompute embeddings for dataset questions
dataset_inputs = [item.get("input", "").lower().strip() for item in dataset]
dataset_answers = [item.get("response", "") for item in dataset]
dataset_embeddings = similarity_model.encode(dataset_inputs, convert_to_tensor=True)

# ✅ Function to detect greetings
def is_greeting(text):
    greetings = ["hello", "hi", "hey", "salam", "assalam o alaikum", "assalamu alaikum", "assalamualaikum"]
    return text.lower().strip() in greetings

# ✅ Function to get the best matching answer
def get_best_answer(user_input):
    if is_greeting(user_input):
        return "Hello! 😊 How can I assist you today with university-related information?"

    user_input_embedding = similarity_model.encode(user_input.lower().strip(), convert_to_tensor=True)
    similarities = util.pytorch_cos_sim(user_input_embedding, dataset_embeddings)[0]

    # ✅ Find the best match and its similarity score
    best_match_index = similarities.argmax().item()
    best_score = similarities[best_match_index].item()

    # ✅ Set a similarity threshold (tune as needed)
    THRESHOLD = 0.65

    if best_score < THRESHOLD:
        return (
             "I'm sorry, I couldn't find an exact answer to your question. "
             "You may kindly try rephrasing your question gently for better results. "
             "Also, feel free to visit the UOE official website for information: https://ue.edu.pk/"
        )

    best_answer = dataset_answers[best_match_index]
    return rephrase_answer(best_answer)

# ✅ Function to rephrase answer using GPT
def rephrase_answer(answer):
    prompt = (
        f"Rephrase the following university-related answer while keeping the meaning unchanged:\n\n"
        f"Original Answer: {answer}\n\n"
        f"Rephrased Answer:"
    )

    payload = {"inputs": prompt}  # ✅ Structured prompt to prevent extra details

    response = requests.post(GPT_MODEL_API, headers=headers, json=payload)

    if response.status_code == 200:
        result = response.json()
        if isinstance(result, list) and result:
            generated_text = result[0].get("generated_text", answer).strip()
            
            # ✅ Ensure only the rephrased answer is returned
            if "Rephrased Answer:" in generated_text:
                return generated_text.split("Rephrased Answer:")[-1].strip()
            return generated_text

    return answer  # ✅ Return original answer if API fails