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import io
from fastapi import FastAPI, File, UploadFile
import subprocess
import os
import requests
import random
from datetime import datetime
from datetime import date
import json
from pydantic import BaseModel
from typing import Annotated
import random
from fastapi import FastAPI, Response
import string
import time
from huggingface_hub import InferenceClient
from fastapi import Form
class Query(BaseModel):
text: str
class Query2(BaseModel):
text: str
class QueryM(BaseModel):
text: str
tokens:int
temp:float
topp:float
topk:float
from fastapi import FastAPI, Request, Depends, UploadFile, File
from fastapi.exceptions import HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=['*'],
allow_credentials=True,
allow_methods=['*'],
allow_headers=['*'],
)
# cred = credentials.Certificate('key.json')
# app1 = firebase_admin.initialize_app(cred)
# db = firestore.client()
# data_frame = pd.read_csv('data.csv')
@app.on_event("startup")
async def startup_event():
print("on startup")
# requests.get("https://audiospace-1-u9912847.deta.app/sendcode")
audio_space="https://audiospace-1-u9912847.deta.app/uphoto"
import threading
from huggingface_hub.inference_api import InferenceApi
client = InferenceClient()
@app.post("/image")
async def get_answer(q: Query ):
text = q.text
try:
global client
imagei = client.text_to_image(text)
byte_array = io.BytesIO()
imagei.save(byte_array, format='JPEG')
response = Response(content=byte_array.getvalue(), media_type="image/png")
return response
except:
return JSONResponse({"status":False})
@app.post("/mistral")
async def get_answer(q: QueryM ):
text = q.text
try:
client = InferenceClient()
generate_kwargs = dict(
max_new_tokens= int(q.tokens),
do_sample=True,
top_p= q.topp,
top_k=int(q.topk),
temperature=q.temp,
)
inputs= text
response = client.post(json={"inputs": inputs, "parameters": generate_kwargs}, model="mistralai/Mistral-7B-Instruct-v0.1")
json_string = response.decode('utf-8')
list_of_dicts = json.loads(json_string)
result_dict = list_of_dicts[0]
x=(result_dict['generated_text'])
x=x.replace(inputs,'')
return JSONResponse({"result":x,"status":True})
except Exception as e:
print(e)
return JSONResponse({"status":False})
@app.post("/zephyr")
async def get_answer(q: QueryM ):
text = q.text
try:
client = InferenceClient()
generate_kwargs = dict(
max_new_tokens= int(q.tokens),
repetition_penalty=1.0,
top_p= q.topp,
top_k=int(q.topk),
temperature=q.temp,
stop= ["</s>", "<|>"]
)
inputs= text
response = client.post(json={"inputs": inputs, "parameters": generate_kwargs}, model="mistralai/Mistral-7B-Instruct-v0.1")
json_string = response.decode('utf-8')
list_of_dicts = json.loads(json_string)
result_dict = list_of_dicts[0]
x=(result_dict['generated_text'])
x=x.replace(inputs,'')
return JSONResponse({"result":x,"status":True})
except Exception as e:
print(e)
return JSONResponse({"status":False})
@app.post("/openchat35")
async def get_answer(q: QueryM ):
text = q.text
try:
client = InferenceClient()
generate_kwargs = dict(
max_new_tokens= int(q.tokens),
repetition_penalty=1.0,
top_p= q.topp,
top_k=int(q.topk),
temperature=q.temp,
stop= ["<|end_of_turn|>", "<??|>"]
)
inputs= text
response = client.post(json={"inputs": inputs, "parameters": generate_kwargs},model="openchat/openchat_3.5")
json_string = response.decode('utf-8')
list_of_dicts = json.loads(json_string)
result_dict = list_of_dicts[0]
x=(result_dict['generated_text'])
x=x.replace(inputs,'')
return JSONResponse({"result":x,"status":True})
except Exception as e:
print(e)
return JSONResponse({"status":False})
@app.post("/image_new")
async def get_answer(q: Query ):
text = q.text
try:
global client
imagei = client.text_to_image(text, model ='openskyml/dalle-3-xl')
byte_array = io.BytesIO()
imagei.save(byte_array, format='JPEG')
response = Response(content=byte_array.getvalue(), media_type="image/png")
return response
except:
return JSONResponse({"status":False})
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