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main.py

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+import re
+import string
+import nltk
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+from transformers import pipeline
+from pyngrok import ngrok
+import nest_asyncio
+from fastapi.responses import RedirectResponse
+
+# Download NLTK resources
+nltk.download('punkt')
+nltk.download('wordnet')
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Text preprocessing functions
+def remove_urls(text):
+    return re.sub(r'http[s]?://\S+', '', text)
+
+def remove_punctuation(text):
+    regular_punct = string.punctuation
+    return re.sub(r'['+regular_punct+']', '', text)
+
+def lower_case(text):
+    return text.lower()
+
+def lemmatize(text):
+    wordnet_lemmatizer = nltk.WordNetLemmatizer()
+    tokens = nltk.word_tokenize(text)
+    return ' '.join([wordnet_lemmatizer.lemmatize(w) for w in tokens])
+
+# Model loading
+lyx_pipe = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+
+# Input data model
+class TextInput(BaseModel):
+    text: str
+
+# Welcome endpoint
+@app.get('/')
+async def welcome():
+    # Redirect to the Swagger UI page
+    return RedirectResponse(url="/docs")
+
+# Sentiment analysis endpoint
+@app.post('/analyze/')
+async def Predict_Sentiment(text_input: TextInput):    
+    text = text_input.text
+
+    # Text preprocessing
+    text = remove_urls(text)
+    text = remove_punctuation(text)
+    text = lower_case(text)
+    text = lemmatize(text)
+
+    # Perform sentiment analysis
+    try:
+        return lyx_pipe(text)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Run the FastAPI app using Uvicorn
+if __name__ == "__main__":
+    # Create ngrok tunnel
+    ngrok_tunnel = ngrok.connect(7860)
+    print('Public URL:', ngrok_tunnel.public_url)
+
+    # Allow nested asyncio calls
+    nest_asyncio.apply()
+
+    # Run the FastAPI app with Uvicorn
+    import uvicorn
+    uvicorn.run(app, port=7860)

test.py

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+from fastapi.testclient import TestClient
+from main import app
+
+client = TestClient(app)
+
+def test_positive_sentiment():
+    response = client.post("/predict", json={"text": "I love this product! It's amazing."})
+    assert response.status_code == 200
+    assert response.json() == {"sentiment": "joy"}  # Positive sentiment corresponds to joy
+
+def test_negative_sentiment():
+    response = client.post("/predict", json={"text": "This product is terrible. I regret buying it."})
+    assert response.status_code == 200
+    assert response.json() == {"sentiment": "anger"}  # Negative sentiment corresponds to anger
+
+def test_neutral_sentiment():
+    response = client.post("/predict", json={"text": "This product is okay. It meets my expectations."})
+    assert response.status_code == 200
+    assert response.json() == {"sentiment": "neutral"}  # Neutral sentiment remains unchanged
+
+def test_anger_sentiment():
+    response = client.post("/predict", json={"text": "This product makes me furious!"})
+    assert response.status_code == 200
+    assert response.json() == {"sentiment": "anger"}  # Emotion of anger
+
+def test_disgust_sentiment():
+    response = client.post("/predict", json={"text": "I find this product revolting."})
+    assert response.status_code == 200
+    assert response.json() == {"sentiment": "disgust"}  # Emotion of disgust
+
+def test_fear_sentiment():
+    response = client.post("/predict", json={"text": "This product scares me."})
+    assert response.status_code == 200
+    assert response.json() == {"sentiment": "fear"}  # Emotion of fear
+
+def test_sadness_sentiment():
+    response = client.post("/predict", json={"text": "This product makes me really sad."})
+    assert response.status_code == 200
+    assert response.json() == {"sentiment": "sadness"}  # Emotion of sadness
+
+def test_surprise_sentiment():
+    response = client.post("/predict", json={"text": "I'm amazed by this product!"})
+    assert response.status_code == 200
+    assert response.json() == {"sentiment": "surprise"}  # Emotion of surprise