import re
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import uvicorn
from typing import Generator
import json  # Asegúrate de que esta línea esté al principio del archivo
import nltk
import os
import google.protobuf  # This line should execute without errors if protobuf is installed correctly
import sentencepiece
from transformers import pipeline, AutoTokenizer,AutoModelForSeq2SeqLM
import spacy


nltk.data.path.append(os.getenv('NLTK_DATA'))

app = FastAPI()

# Initialize the InferenceClient with your model
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

class Item(BaseModel):
    prompt: str
    history: list
    system_prompt: str
    temperature: float = 0.8
    max_new_tokens: int = 9000
    top_p: float = 0.15
    repetition_penalty: float = 1.0

def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate_stream(item: Item) -> Generator[bytes, None, None]:
    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
    generate_kwargs = {
        "temperature": item.temperature,
        "max_new_tokens": item.max_new_tokens,
        "top_p": item.top_p,
        "repetition_penalty": item.repetition_penalty,
        "do_sample": True,
        "seed": 42,  # Adjust or omit the seed as needed
    }

    # Stream the response from the InferenceClient
    for response in client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True):
        # This assumes 'details=True' gives you a structure where you can access the text like this
        chunk = {
            "text": response.token.text,
            "complete": response.generated_text is not None  # Adjust based on how you detect completion
        }
        yield json.dumps(chunk).encode("utf-8") + b"\n"

@app.post("/generate/")
async def generate_text(item: Item):
    # Stream response back to the client
    return StreamingResponse(generate_stream(item), media_type="application/x-ndjson")

# Load spaCy model
nlp = spacy.load("en_core_web_sm")

class TextRequest(BaseModel):
    text: str

def preprocess_text(text: str) -> str:
    # Normalize whitespace and strip punctuation
    text = re.sub(r'\s+', ' ', text.strip())
    text = re.sub(r'[^\w\s]', '', text)
    return text

def reduce_tokens(text: str):
    # Process the text with spaCy
    doc = nlp(text)
    # Select sentences that might be more important - this is a simple heuristic
    important_sentences = []
    for sent in doc.sents:
        if any(tok.dep_ == 'ROOT' for tok in sent):
            important_sentences.append(sent.text)
    # Join selected sentences to form the reduced text
    reduced_text = ' '.join(important_sentences)
    # Tokenize the reduced text to count the tokens
    reduced_doc = nlp(reduced_text)  # Ensure this line is correctly aligned
    token_count = len(reduced_doc)
    return reduced_text, token_count

@app.post("/summarize")
async def summarize(request: TextRequest):
    try:
        processed_text = preprocess_text(request.text)
        reduced_text, token_count = reduce_tokens(processed_text)
        return {
            "reduced_text": reduced_text,
            "token_count": token_count
        }
    
    except Exception as e:
        print(f"Error during token reduction: {e}")
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)