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Dockerfile

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+FROM python:3.8.18
+
+WORKDIR /
+
+# Copy requirements.txt to the container
+COPY requirements.txt ./
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Add a non-root user to run the application
+RUN useradd -m -u 1000 user
+
+# Set the user and home directory environment variables
+USER user
+ENV HOME=/home/user \
+  PATH=/home/user/.local/bin:$PATH
+
+# Create the application directory
+WORKDIR $HOME/app
+
+# Copy the application code and model files
+COPY --chown=user . $HOME/app/
+
+# Expose the port the FastAPI app runs on
+EXPOSE 7860
+
+# Command to run the FastAPI app
+CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "7860"]

api.py

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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)

requirements.txt

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+fastapi==0.99.1
+uvicorn==0.27.1
+transformers==4.38.2