import pandas as pd
import openai
df=pd.read_csv("data/faq_data.csv")
print(df)

from openai import OpenAI
#Feel free to use chatgpt or other models - Google offere free gemini api hence I have used this
model_name = {
    "OpenAI GPT-4": {
        "name": "gpt-4",
        "url": "https://api.openai.com/v1/"  # Replace with correct OpenAI base URL
    },
    "Gemini 1.5Flash": {
        "name": "gemini-1.5-flash",
        "url": "https://generativelanguage.googleapis.com/v1beta/"
    },
    "Gemini 2.0Flash": {
        "name": "gemini-2.0-flash",
        "url": "https://generativelanguage.googleapis.com/v1beta/"
    },
    "Together AI": {
        "name": "mistralai/Mixtral-8x7B-Instruct-v0.1",
        "url": "https://api.together.xyz/v1/"  # Replace with correct Together base URL
    }
}


import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np


# Optional: chunk answers into smaller pieces (e.g. 200 tokens) for better retrieval
def chunk_text(text, max_tokens=200):
    words = text.split()
    for i in range(0, len(words), max_tokens):
        yield " ".join(words[i:i+max_tokens])

chunks = []
metadata = []
for idx, row in df.iterrows():
    for c in chunk_text(row['answer']):
        chunks.append(c)
        metadata.append({
            "topic": row['topic'],
            "question": row['question'],
            "answer_chunk": c
        })


from sentence_transformers import SentenceTransformer
import numpy as np
#I am using the huggingface model to generate embeddings , If the data was large once can try & explore other embeddings
embedder = SentenceTransformer('all-MiniLM-L6-v2')

# Embed chunks
chunk_vectors = embedder.encode(chunks, convert_to_numpy=True)

# Build FAISS index as before
import faiss

dimension = chunk_vectors.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(chunk_vectors)

# For query embedding function
def embed_query(query):
    return embedder.encode([query], convert_to_numpy=True)[0]
tfidf_vectorizer = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf_vectorizer.fit_transform(df['question'])
def retrieve_similar_answer_chunks(query, k=5):
    q_vec = embed_query(query)
    D, I = index.search(np.array([q_vec]).astype('float32'), k)
    return [metadata[i] for i in I[0]]

def retrieve_similar_questions(query, k=5):
    q_tfidf = tfidf_vectorizer.transform([query])
    scores = (tfidf_matrix @ q_tfidf.T).toarray()
    topk_idx = scores[:,0].argsort()[-k:][::-1]
    return df.iloc[topk_idx][['topic', 'question', 'answer']].to_dict(orient='records')
def hybrid_retrieve(query, k=5):
    answer_chunks = retrieve_similar_answer_chunks(query, k)
    question_hits = retrieve_similar_questions(query, k)
    combined_contexts = answer_chunks + question_hits

    # Deduplicate if needed
    seen = set()
    filtered = []
    for c in combined_contexts:
        key = (c.get('topic'), c.get('question'))
        if key not in seen:
            filtered.append(c)
            seen.add(key)
    return filtered

def generate_answer(message,model_choice,api_key):
    contexts = hybrid_retrieve(message, k=5)
    context_text = "\n\n".join([f"Q: {c['question']}\nA: {c.get('answer') or c.get('answer_chunk')}" for c in contexts])

    messages = [
        {
            "role": "system",
            "content": (
            "You are an expert airline assistant answering user queries based on the provided context."
            " Use the context to generate a helpful, factual, self-contained answer."
            "If the context doesn't help and you are unable to answer, please reply to the user to reach out to our customer service call center at 1 800 800 000 or email us at customercare@airlinex.com."
            "If the user is just greeting (e.g., says 'hi', 'hello', 'good morning', etc.), you can greet back briefly and ask how you may assist."
            "If the user is asking something random and unrelated to airline services and it's not a greeting, reply: 'I don't understand your question, can you please rephrase?'"
        )        },
        {
            "role": "user",
            "content": f"Context:\n{context_text}\n\nQuestion: {message}\nAnswer:"
        }
    ]
    config = model_name[model_choice]
    openai=OpenAI(
        base_url=config["url"],
        api_key=api_key,
    )
    

    response = openai.chat.completions.create(
        model="gemini-1.5-flash",
        messages=messages,
        temperature=0.7,
        max_tokens=700,
    )
    return response.choices[0].message.content