Update app.py

app.py

@@ -1,28 +1,16 @@
-import firebase_admin
-from firebase_admin import credentials
-from firebase_admin import firestore
 import io
 from fastapi import FastAPI, File, UploadFile
-from werkzeug.utils import secure_filename
-import speech_recognition as sr
 import subprocess
 import os
 import requests
 import random
-import pandas as pd
-from pydub import AudioSegment
 from datetime import datetime
 from datetime import date
-import numpy as np
-from sklearn.ensemble import RandomForestRegressor
-import shutil
 import json
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
 from pydantic import BaseModel
 from typing import Annotated
-from transformers import BertTokenizerFast, EncoderDecoderModel
-import torch
 import random
+from fastapi import FastAPI, Response
 import string
 import time
 from huggingface_hub import InferenceClient
@@ -40,13 +28,12 @@ class Query2(BaseModel):
     filename:str
     host:str
    
-# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-# tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization')
-# model = EncoderDecoderModel.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization').to(device)
-
-
-
-
+class QueryM(BaseModel):
+    text: str
+    tokens:int
+    temp:float
+    topp:float
+    topk:float
 
 
 
@@ -81,15 +68,66 @@ async def startup_event():
 
 audio_space="https://audiospace-1-u9912847.deta.app/uphoto"
 
-# @app.post("/code")
-# async def get_code(request: Request):
-#     data = await request.form()
-#     code = data.get("code")
-#     global audio_space
-#     print("code ="+code)
-#     audio_space= audio_space+code
-
 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})
+    
+    
+
+
+
+
+
+
+
+
+
+
+
+
+
+''' to be removed when main code is updated '''
+
 @app.post("/")
 async def get_answer(q: Query ):
 
@@ -137,13 +175,12 @@ async def get_answer(q: Query2 ):
 
 import requests
 import io
-import torch
 import io
 from PIL import Image
 import json
 
 
-client = InferenceClient()
+
 # client = InferenceClient(model="SG161222/Realistic_Vision_V1.4")
 
     
@@ -180,3 +217,9 @@ def do_ML(filename:str,text:str,code:str,host:str):
         
 
 
+    
+
+
+
+
+