from fastapi import FastAPI from pydantic import BaseModel from calculator import calculate from sentimentAnalysis import sentimentAnalysis from customerSupport import customerConverstaion class User_input(BaseModel): sentence:str operation:str x:float y:float app = FastAPI() @app.get("/hello") def greet_json(): return {"Hello": "World!"} @app.post("/calculate") def calculate_func(input:User_input): res= calculate(input.operation, input.x, input.y) return res import requests # def query(API_URL, headers, payload): # response = requests.post(API_URL, headers=headers, json=payload) # print(response) # return response @app.post("/HFAPI") def HF_API(): # API_TOKEN="" # API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2" # headers = {"Authorization": f"Bearer {API_TOKEN}"} # data = query(API_URL,headers, { # "inputs": "Can you please let us know more details about your ", # }) API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2" headers = {"Authorization": "Bearer ......................q"} def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() output = query({ "inputs": "Can you please let us know more details about India? ", }) return output[0]["generated_text"] @app.post("/sentimentAnalysis") def sentimentAnalysis_func(input:User_input): res= sentimentAnalysis(input.sentence) return res @app.post("/getReply") def getReply_func(input:User_input): res= customerConverstaion(input.sentence) return res @app.post("/hf_spaces") def HF_interact(): from huggingface_hub import HfApi # Initialize API client api = HfApi() # Replace these with your values repo_id = 'DSU-FDP/Sample-API' token = '' # Authenticate api.pause_space(repo_id=repo_id) # List all Spaces (not pausing, just showing how to interact) spaces = api.list_spaces() print(spaces) # Example action: delete a space (be cautious with this!) # api.delete_repo(repo_id, token=token)