Create app.py

app.py

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+from fastapi import FastAPI, HTTPException, UploadFile, File
+from pydantic import BaseModel
+from aitextgen import aitextgen
+from sklearn.datasets import fetch_20newsgroups
+import nltk
+import spacy
+from transformers import pipeline, WhisperForConditionalGeneration, WhisperProcessor
+from transformers import TTSModel, TTSProcessor
+from audiocraft.models import MusicGen
+from diffusers import StableDiffusionPipeline
+import os
+from typing import List
+
+# Descargar nltk y cargar spacy
+nltk.download('punkt')
+nltk.download('stopwords')
+spacy_model = spacy.load('en_core_web_sm')
+
+app = FastAPI()
+
+# Variables globales para almacenar los modelos
+global aitextgen_model, hf_model, musicgen_model, image_generation_model, whisper_model, whisper_processor, tts_model, tts_processor, newsgroups
+aitextgen_model = None
+hf_model = None
+musicgen_model = None
+image_generation_model = None
+whisper_model = None
+whisper_processor = None
+tts_model = None
+tts_processor = None
+newsgroups = None
+
+# Funciones para cargar los modelos solo una vez
+def load_aitextgen_model():
+    global aitextgen_model
+    if aitextgen_model is None:
+        aitextgen_model = aitextgen()
+    return aitextgen_model
+
+def load_hf_model():
+    global hf_model
+    if hf_model is None:
+        hf_model = pipeline('text-generation', model='gpt2')
+    return hf_model
+
+def load_musicgen_model():
+    global musicgen_model
+    if musicgen_model is None:
+        musicgen_model = MusicGen.get_pretrained('small')
+    return musicgen_model
+
+def load_image_generation_model():
+    global image_generation_model
+    if image_generation_model is None:
+        image_generation_model = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+    return image_generation_model
+
+def load_whisper_model():
+    global whisper_model, whisper_processor
+    if whisper_model is None:
+        whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
+        whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+    return whisper_model, whisper_processor
+
+def load_tts_model():
+    global tts_model, tts_processor
+    if tts_model is None:
+        tts_model = TTSModel.from_pretrained("facebook/tts_transformer-tts")
+        tts_processor = TTSProcessor.from_pretrained("facebook/tts_transformer-tts")
+    return tts_model, tts_processor
+
+def load_newsgroups():
+    global newsgroups
+    if newsgroups is None:
+        newsgroups = fetch_20newsgroups(subset='all').data
+    return newsgroups
+
+class TextRequest(BaseModel):
+    prompt: str
+    max_length: int = 50
+
+class MusicRequest(BaseModel):
+    prompt: str
+    duration: float = 10.0
+
+class ImageRequest(BaseModel):
+    prompt: str
+    height: int = 512
+    width: int = 512
+
+class TTSRequest(BaseModel):
+    text: str
+
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the Text, Music Generation, Image Generation, Whisper, and TTS API!"}
+
+@app.post("/generate/")
+def generate_text(request: TextRequest):
+    aitextgen_model = load_aitextgen_model()
+    generated_text = aitextgen_model.generate(prompt=request.prompt, max_length=request.max_length)
+    return {"generated_text": generated_text}
+
+@app.post("/hf_generate/")
+def hf_generate_text(request: TextRequest):
+    hf_model = load_hf_model()
+    generated_text = hf_model(request.prompt, max_length=request.max_length)
+    return {"generated_text": generated_text[0]['generated_text']}
+
+@app.post("/music/")
+def generate_music(request: MusicRequest):
+    musicgen_model = load_musicgen_model()
+    audio = musicgen_model.generate([request.prompt], durations=[request.duration])
+    musicgen_model.save_wav(audio[0], 'generated_music.wav')
+    return {"message": "Music generated successfully", "audio_file": "generated_music.wav"}
+
+@app.post("/generate_image/")
+def generate_image(request: ImageRequest):
+    image_generation_model = load_image_generation_model()
+    image = image_generation_model(request.prompt, height=request.height, width=request.width).images[0]
+    image_path = "generated_image.png"
+    image.save(image_path)
+    return {"message": "Image generated successfully", "image_file": "generated_image.png"}
+
+@app.post("/transcribe/")
+async def transcribe_audio(file: UploadFile = File(...)):
+    whisper_model, whisper_processor = load_whisper_model()
+    audio_input = await file.read()
+    audio_input = whisper_processor(audio_input, return_tensors="pt").input_features
+
+    with torch.no_grad():
+        predicted_ids = whisper_model.generate(audio_input)
+    transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+
+    return {"transcription": transcription}
+
+@app.post("/tts/")
+def text_to_speech(request: TTSRequest):
+    tts_model, tts_processor = load_tts_model()
+    audio = tts_model.generate(request.text)
+    audio_path = "generated_speech.wav"
+    tts_model.save_wav(audio, audio_path)
+    return {"message": "Speech generated successfully", "audio_file": "generated_speech.wav"}
+
+@app.get("/newsgroups/")
+def get_newsgroups():
+    newsgroups_data = load_newsgroups()
+    return {"newsgroups": newsgroups_data[:5]}
+
+@app.post("/process/")
+def process_text(text: str):
+    tokens = nltk.word_tokenize(text)
+    doc = spacy_model(text)
+    return {
+        "tokens": tokens,
+        "entities": [(ent.text, ent.label_) for ent in doc.ents]
+    }