main.py

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 = 4000
    top_p: float = 0.15
    repetition_penalty: float = 1.0

def format_prompt(current_prompt, history):
    formatted_history = "<s>"
    for entry in history:
        if entry["role"] == "user":
            formatted_history += f"[USER] {entry['content']} [/USER]"
        elif entry["role"] == "assistant":
            formatted_history += f"[ASSISTANT] {entry['content']} [/ASSISTANT]"
    formatted_history += f"[USER] {current_prompt} [/USER]</s>"
    return formatted_history


def generate_stream(item: Item) -> Generator[bytes, None, None]:
    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
    # Estimate token count for the formatted_prompt
    input_token_count = len(nltk.word_tokenize(formatted_prompt))  # NLTK tokenization

    # Ensure total token count doesn't exceed the maximum limit
    max_tokens_allowed = 32768
    max_new_tokens_adjusted = max(1, min(item.max_new_tokens, max_tokens_allowed - input_token_count))

    generate_kwargs = {
        "temperature": item.temperature,
        "max_new_tokens": max_new_tokens_adjusted,
        "top_p": item.top_p,
        "repetition_penalty": item.repetition_penalty,
        "do_sample": True,
        "seed": 42,
    }

    # 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"


class SummarizeRequest(BaseModel):
    text: str

@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

def segment_text(text: str, max_tokens=500):  # Setting a conservative limit below 512
    doc = nlp(text)
    segments = []
    current_segment = []
    current_length = 0

    for sent in doc.sents:
        sentence = sent.text.strip()
        sentence_length = len(sentence.split())  # Counting words for simplicity

        if sentence_length > max_tokens:
            # Split long sentences into smaller chunks if a single sentence exceeds max_tokens
            words = sentence.split()
            while words:
                part = ' '.join(words[:max_tokens])
                segments.append(part)
                words = words[max_tokens:]
        elif current_length + sentence_length > max_tokens:
            segments.append(' '.join(current_segment))
            current_segment = [sentence]
            current_length = sentence_length
        else:
            current_segment.append(sentence)
            current_length += sentence_length

    if current_segment:  # Add the last segment
        segments.append(' '.join(current_segment))

    return segments


classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")


def classify_segments(segments):
    classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
    classified_segments = []
    
    for segment in segments:
        try:
            if len(segment.split()) <= 512:  # Double-check to avoid errors
                result = classifier(segment)
                classified_segments.append(result)
            else:
                classified_segments.append({"error": f"Segment too long: {len(segment.split())} tokens"})
        except Exception as e:
            classified_segments.append({"error": str(e)})

    return classified_segments


@app.post("/process_document")
async def process_document(request: TextRequest):
    try:
        processed_text = preprocess_text(request.text)
        segments = segment_text(processed_text)
        classified_segments = classify_segments(segments)

        return {
            "classified_segments": classified_segments
        }
    except Exception as e:
        print(f"Error during document processing: {e}")
        raise HTTPException(status_code=500, detail=str(e))



@app.post("/summarize")
async def summarize(request: TextRequest):
    try:
        # Preprocess and segment the text
        processed_text = preprocess_text(request.text)
        segments = segment_text(processed_text)

        # Classify each segment safely
        classified_segments = []
        for segment in segments:
            try:
                result = classifier(segment)
                classified_segments.append(result)
            except Exception as e:
                print(f"Error classifying segment: {e}")
                classified_segments.append({"error": str(e)})

        # Optional: Reduce tokens or summarize
        reduced_texts = []
        for segment in segments:
            try:
                reduced_text, token_count = reduce_tokens(segment)
                reduced_texts.append((reduced_text, token_count))
            except Exception as e:
                print(f"Error during token reduction: {e}")
                reduced_texts.append(("Error", 0))

        return {
            "classified_segments": classified_segments,
            "reduced_texts": reduced_texts
        }
    
    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)