app.py

from flask import Flask,request,render_template,send_file,jsonify
import os
from transformers import AutoTokenizer, AutoModel
import anvil.server
import pathlib
import textwrap
import google.generativeai as genai
import import_ipynb
from library import call_gpt, call_gemini
from background_service import BackgroundTaskService

anvil.server.connect('PLMOIU5VCGGUOJH2XORIBWV3-ZXZVFLWX7QFIIAF4')

app=Flask(__name__)
MESSAGED={'title':'API Server',
          'messageL':['published server functions:','encode(text)', 'call_gemini(text)']}

tokenizer = AutoTokenizer.from_pretrained('allenai/specter')
encoder = AutoModel.from_pretrained('allenai/specter')

# GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')
# genai.configure(api_key=GOOGLE_API_KEY)

service=BackgroundTaskService(max_tasks=10)
service.register(call_gpt)
service.register(call_gemini)

@anvil.server.callable
def launch(func_name,*args):
    global service
    # Launch task
    task_id = service.launch_task(func_name, *args)
    print(f"Task launched with ID: {task_id}")
    return task_id

@anvil.server.callable
def poll(task_id):
    global service
    # Poll for completion; if not complete return "In Progress" else return result
    result = service.get_result(task_id)
    if result=='No such task': return str(result)
    elif result!='In Progress': 
        del service.results[task_id]
        if isinstance(result, (int, float, str, list, dict, tuple)): 
            return result
        else: 
            print(str(result))
            return str(result)
    else: return str(result)

# @anvil.server.callable
# def call_gemini(text):
#     model = genai.GenerativeModel('gemini-pro')
#     response = model.generate_content(text)
#     return response.text

@anvil.server.callable
def encode_anvil(text):
    inputs = tokenizer(text, padding=True, truncation=True, 
                       return_tensors="pt", max_length=512)
    result = encoder(**inputs)
    embeddings = result.last_hidden_state[:, 0, :]
    emb_array = embeddings.detach().numpy()
    embedding=emb_array.tolist()
    return embedding

@app.route('/encode',methods=['GET','POST'])
def encode():
    print(request)
    if request.method=='GET':
        text=request.args.get('text')
    elif request.method=='POST':
        data=request.get_json()
        if 'text' in data: text=data["text"]
    if text=='' or text is None: return -1
    inputs = tokenizer(text, padding=True, truncation=True, 
                       return_tensors="pt", max_length=512)
    result = encoder(**inputs)
    embeddings = result.last_hidden_state[:, 0, :]
    emb_array = embeddings.detach().numpy()
    embedding=emb_array.tolist()
    return jsonify({'embedding': embedding})

@app.route("/file/<string:filename>")
def return_file(filename):
    return send_file('./data/'+filename)

@app.route('/run',methods=['GET','POST'])
def run_script():
    script=''
    # print(request.method)
    print(request)
    if request.method=='GET': 
        script=request.args.get('script')
        print('I am in get')
    elif request.method=='POST':
        print('I am in post')
        data=request.get_json()
        if 'script' in data: script=data['script']
    if script=='' or script is None: return 'INVALID'
    os.system(script+' > ./out.txt')
    with open('./out.txt','r') as f: output=f.read()
    return output

@app.route('/',methods=['GET', 'POST'])
def home():
    return render_template('home.html',messageD=MESSAGED)

if __name__=='__main__':
    app.run(host="0.0.0.0", port=7860)