Create aap.py

aap.py

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+from fastapi import FastAPI
+import nest_asyncio
+from pyngrok import ngrok
+import uvicorn
+import requests
+import torch
+# from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+# tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+# model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+
+app = FastAPI()
+
+url = "https://sentiment-analysis9.p.rapidapi.com/sentiment"
+#user_input = input("Please enter a text for sentiment analysis: ")
+def call_sentiment_api(user_input):
+  payload = [
+    {
+      "id": "1",
+      "language": "en",
+      "text":user_input
+    }
+  ]
+  headers = {
+    "content-type": "application/json",
+    "Accept": "application/json",
+    "X-RapidAPI-Key": "5cf8fcaf61msh613f010a34f3576p1953e5jsn110a1e6c667d",
+    "X-RapidAPI-Host": "sentiment-analysis9.p.rapidapi.com"
+  }
+
+  response = requests.post(url, json=payload, headers=headers)
+
+  print(response.json())
+  return response.json()
+
+# def sentiment_model_hf(user_input):
+#   inputs = tokenizer(user_input, return_tensors="pt")
+#   with torch.no_grad():
+#     logits = model(**inputs).logits
+#   predicted_class_id = logits.argmax().item()
+
+#   return model.config.id2label[predicted_class_id]
+
+
+@app.get('/sentiment_ra/{user_input}')
+async def sentiment(user_input):
+  return call_sentiment_api(user_input)
+
+# @app.get('/sentiment_hf/{user_input}')
+# async def sentiment_hf(user_input):
+#   return sentiment_model_hf(user_input)
+
+
+@app.get('/a')
+async def abc():
+
+
+  return "Hello Atom Camp --  Our first Endpoint"
+
+@app.get('/2nd')
+async def atom():
+
+
+  return "its our 2nd endpoint"
+
+@app.get('/{multiply}')
+async def atom(multiply):
+
+
+  return multiply*10
+
+@app.get('/')
+async def html():
+
+  return "Welcome to Our FastAPI Endpoints"
+
+ngrok_tunnel = ngrok.connect(8000)
+print('Public URL:', ngrok_tunnel.public_url)
+nest_asyncio.apply()
+uvicorn.run(app, port=8000)