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from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware # Importa il middleware CORS
from pydantic import BaseModel
from huggingface_hub import InferenceClient
from datetime import datetime
from gradio_client import Client
import base64
import requests
import os
app = FastAPI()
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class InputData(BaseModel):
input: str
temperature: float = 0.2
max_new_tokens: int = 30000
top_p: float = 0.95
repetition_penalty: float = 1.0
def format_prompt(message, history):
prompt = "<s>"
#with open('Manuale.txt', 'r') as file:
# manual_content = file.read()
# prompt += f"Leggi questo manuale dopo ti farò delle domande: {manual_content}"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
now = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")
prompt += f"[{now}] [INST] {message} [/INST]"
return prompt
@app.post("/Genera")
def read_root(request: Request, input_data: InputData):
input_text = input_data.input
temperature = input_data.temperature
max_new_tokens = input_data.max_new_tokens
top_p = input_data.top_p
repetition_penalty = input_data.repetition_penalty
history = [] # Puoi definire la history se necessario
generated_response = generate(input_text, history, temperature, max_new_tokens, top_p, repetition_penalty)
return {"response": generated_response}
@app.get("/Immagine")
def generate_image():
client = Client("https://openskyml-fast-sdxl-stable-diffusion-xl.hf.space/--replicas/545b5tw7n/")
result = client.predict(
"a giant monster hybrid of dragon and spider, in dark dense foggy forest ",
"",
25,
5,
1024,
1024,
453666937,
fn_index=0
)
image_url = os.path.abspath(os.path.dirname(__file__)) + result
return {"response": image_url}
@app.get("/Test")
def generate_image():
result = "a giant monster hybrid of dragon and spider, in dark dense foggy forest "
return {"response": result}
@app.get("/")
def read_general():
return {"response": "Benvenuto. Per maggiori info vai a /docs"} # Restituisci la risposta generata come JSON
def generate(prompt, history, temperature=0.2, max_new_tokens=30000, top_p=0.95, repetition_penalty=1.0):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(prompt, history)
output = client.text_generation(formatted_prompt, **generate_kwargs, stream=False, details=False)
return output
#stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=False, return_full_text=False)
# Accumula l'output in una lista
#output_list = []
#for response in stream:
# output_list.append(response.token.text)
#return iter(output_list) # Restituisci la lista come un iteratore |