import os
import json
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List
from transformers import pipeline
from sentence_transformers import SentenceTransformer
import faiss
import gradio as gr
from gradio import mount_gradio_app

# ------------------- Config ------------------- #
DATA_PATH = "./dataset/pentagon_core.json"
EMBEDDING_MODEL = "./models/all-MiniLM-L6-v2"
QA_MODEL = "./models/bart-large-cnn"
DEVICE = "cuda" if os.environ.get("USE_CUDA") == "1" else "cpu"

# ------------------- Load Models ------------------- #
embedder = SentenceTransformer(EMBEDDING_MODEL)
qa_model = pipeline("text2text-generation", model=QA_MODEL, device=0 if DEVICE == "cuda" else -1)

# ------------------- Load Dataset + Index ------------------- #
if os.path.exists(DATA_PATH):
    with open(DATA_PATH, "r") as f:
        knowledge_base = json.load(f)
else:
    knowledge_base = []

texts = [item["content"] for item in knowledge_base]
embeddings = embedder.encode(texts, convert_to_tensor=True)
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings.cpu().detach().numpy())

# ------------------- FastAPI App ------------------- #
app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # For development
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# --------- Upload Endpoint --------- #
class UploadData(BaseModel):
    content: str

@app.post("/upload/")
def upload_knowledge(data: UploadData):
    global knowledge_base, index

    knowledge_base.append({"content": data.content})
    with open(DATA_PATH, "w") as f:
        json.dump(knowledge_base, f, indent=2)

    new_embedding = embedder.encode([data.content], convert_to_numpy=True)
    index.add(new_embedding)

    return {"message": "Data uploaded and indexed."}

# --------- Ask Endpoint --------- #
@app.get("/ask/")
def ask(question: str, top_k: int = 3):
    question_embedding = embedder.encode([question], convert_to_numpy=True)
    distances, indices = index.search(question_embedding, top_k)
    context = " ".join([knowledge_base[i]["content"] for i in indices[0]])

    prompt = (
        f"Context: {context}\n\n"
        f"Answer the following question based only on the above context:\n"
        f"{question}\n\nAnswer:"
    )
    output = qa_model(prompt, max_length=256, do_sample=False)[0]["generated_text"]

    return {
        "question": question,
        "context_used": context,
        "answer": output.strip()
    }

# --------- Gradio UI --------- #
def gradio_upload(file):
    if file is None:
        return "No file selected."

    try:
        content = str(file)  # Works for NamedString
        import requests

        base_url = os.getenv("HF_SPACE_URL", "http://localhost:7860")
        response = requests.post(f"{base_url}/upload/", json={"content": content})

        if response.status_code == 200:
            return "✅ Data successfully uploaded and indexed!"
        else:
            return f"❌ Failed: {response.text}"
    except Exception as e:
        return f"❌ Error: {str(e)}"


gr_app = gr.Interface(
    fn=gradio_upload,
    inputs=gr.File(label="Upload .txt or .json file"),
    outputs="text",
    title="Upload Knowledge",
)

# Mount Gradio at /ui
app = mount_gradio_app(app, gr_app, path="/ui")