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# Built-in
from pathlib import Path
import uvicorn
import re
# Dependencies for FastAPI
from fastapi import FastAPI
from fastapi.responses import RedirectResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline
# Class for Text Body
class Paragraph(BaseModel):
input: str
# Load the EmoRoBERTa Model
tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa")
model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa")
emotion = pipeline('sentiment-analysis', model='arpanghoshal/EmoRoBERTa', return_all_scores= True)
# Start the app
app = FastAPI()
# Setup CORS policy
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
def remove_unknown_symbols(text):
# Define a regular expression pattern to match characters that are not within the range of alphanumeric, space, and common punctuation characters
pattern = re.compile(r'[^A-Za-z0-9\s.,?!\'"-]')
# Replace unknown symbols with an empty string
cleaned_text = re.sub(pattern, '', text)
# Truncate the text if its length exceeds 1020 characters
return cleaned_text[:1020]
# APIs
@app.get("/")
async def docs():
return RedirectResponse(url="/docs")
@app.post("/emoroberta")
async def predict_emotions_emoroberta(paragraph : Paragraph):
# Split the huge chunk of text into a list of strings
text_list = [text.strip() for text in re.split(r'[.!?;\n]', paragraph.input) if text.strip()]
# Create a list to store predictions per text
predictions_per_text = []
for text in text_list:
cleaned_text = remove_unknown_symbols(text)
emotions = emotion(cleaned_text)[0]
predictions_per_text.append(emotions)
# Create a dictionary to aggregate scores for each label
total = {}
# Iterate over each list and aggregate the scores
for prediction in predictions_per_text:
for emotion_dict in prediction:
label = emotion_dict['label']
score = emotion_dict['score']
total[label] = total.get(label, 0) + score
# Convert the dictionary to a list of dictionaries
result = [{"label": label, "score": score} for label, score in total.items()]
# Sort the result in descending order based on score
sorted_result = sorted(result, key=lambda x: x['score'], reverse=True)
return {"predictions": sorted_result}
# if __name__ == "__main__":
# uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True) |