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Dockerfile

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+# syntax=docker/dockerfile:1
+
+# Comments are provided throughout this file to help you get started.
+# If you need more help, visit the Dockerfile reference guide at
+# https://docs.docker.com/go/dockerfile-reference/
+
+# Want to help us make this template better? Share your feedback here: https://forms.gle/ybq9Krt8jtBL3iCk7
+
+ARG PYTHON_VERSION=3.12.3
+FROM python:3.12.3-slim as base
+
+# Prevents Python from writing pyc files.
+ENV PYTHONDONTWRITEBYTECODE=1
+
+# Keeps Python from buffering stdout and stderr to avoid situations where
+# the application crashes without emitting any logs due to buffering.
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+# Copy the requirements file into the container at /app
+#COPY requirements.txt .
+
+# Create a non-privileged user that the app will run under.
+# See https://docs.docker.com/go/dockerfile-user-best-practices/
+ARG UID=10001
+RUN adduser \
+    --disabled-password \
+    --gecos "" \
+    --home "/nonexistent" \
+    --shell "/sbin/nologin" \
+    --no-create-home \
+    --uid "${UID}" \
+    appuser
+
+# Download dependencies as a separate step to take advantage of Docker's caching.
+# Leverage a cache mount to /root/.cache/pip to speed up subsequent builds.
+# Leverage a bind mount to requirements.txt to avoid having to copy them into
+# into this layer.
+RUN --mount=type=cache,target=/root/.cache/pip \
+    --mount=type=bind,source=requirements.txt,target=requirements.txt \
+   python -m pip install -r requirements.txt
+# Install the dependencies from the requirements.txt file
+
+# Install pyngrok
+  #RUN pip install pyngrok
+
+# Switch to the non-privileged user to run the application.
+USER appuser
+
+# Set the TRANSFORMERS_CACHE environment variable
+ENV TRANSFORMERS_CACHE=/tmp/.cache/huggingface
+
+# Create the cache folder with appropriate permissions
+RUN mkdir -p $TRANSFORMERS_CACHE && chmod -R 777 $TRANSFORMERS_CACHE
+
+# Copy the source code into the container.
+COPY . .
+
+# Expose the port that the application listens on.
+EXPOSE 8000
+
+# Run the application.
+CMD uvicorn 'main:app' --host=0.0.0.0 --port=8000

main.py

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+from fastapi import FastAPI, HTTPException
+from transformers import pipeline
+from pydantic import BaseModel
+import nest_asyncio
+#from pyngrok import ngrok
+from fastapi.responses import RedirectResponse
+
+app = FastAPI()
+
+# Load the model
+text_classifier = pipeline("text-classification", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
+
+# Input data model
+class TextInput(BaseModel):
+    text: str
+
+@app.post('/analyze')
+async def Predict_Sentiment(text_input: TextInput):
+    text = text_input.text
+
+    # Validate input text
+    if not text.strip():  # Check if text is empty or contains only whitespace
+        raise HTTPException(status_code=400, detail="Input text is empty or contains only whitespace.")
+    elif text.strip() == "--":  # Check if text is "--"
+        raise HTTPException(status_code=400, detail="Invalid input text.")
+    elif text.isdigit():  # Check if text contains only digits
+        raise HTTPException(status_code=400, detail="Input text contains only digits.")
+    elif not any(c.isalpha() for c in text):  # Check if text contains any alphabetic characters
+        raise HTTPException(status_code=400, detail="Input text contains no alphabetic characters.")
+
+    # Perform sentiment analysis
+    try:
+        return text_classifier(text)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get('/')
+async def html():
+    return "Welcome to Financial Sentiment Analysis API"
+
+
+#ngrok_tunnel = ngrok.connect(8000)
+#print('Public URL:', ngrok_tunnel.public_url)
+nest_asyncio.apply()
+#uvicorn.run(app, port=8000)
+
+

requirements.txt

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+fastapi
+uvicorn
+pydantic
+transformers
+nest-asyncio
+torch
+pytest

test_main.py

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+from fastapi.testclient import TestClient
+from main import app
+from main import TextInput
+
+client = TestClient(app)
+
+def test_positive_sentiment():
+    response = client.post("/analyze", json={"text": "Profits are up by 5 million this year."})
+    assert response.status_code == 200
+    # Ensure that the response is parsed as a list of dictionaries
+    data_list = response.json()
+    assert isinstance(data_list, list)
+    # Check if the first item in the list has a "label" key
+    assert data_list[0]["label"] == "positive"
+
+def test_negative_sentiment():
+    response = client.post("/analyze", json={"text": "Operating profit totaled EUR 9.4 mn , down from EUR 11.7 mn in 2004 ."})
+    assert response.status_code == 200
+    # Ensure that the response is parsed as a list of dictionaries
+    data_list = response.json()
+    assert isinstance(data_list, list)
+    # Check if the first item in the list has a "label" key
+    assert data_list[0]["label"] == "negative"
+
+def test_neutral_sentiment():
+    response = client.post("/analyze", json={"text": "The profits are same as last year"})
+    assert response.status_code == 200
+    # Ensure that the response is parsed as a list of dictionaries
+    data_list = response.json()
+    assert isinstance(data_list, list)
+    # Check if the first item in the list has a "label" key
+    assert data_list[0]["label"] == "neutral"
+
+
+

test_results.txt

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+============================= test session starts =============================
+platform win32 -- Python 3.12.3, pytest-8.2.0, pluggy-1.5.0
+rootdir: C:\Users\Shahab\Desktop\Fin-Data-Sentiment-API
+plugins: anyio-4.3.0
+collected 3 items
+
+test_main.py ...                                                         [100%]
+
+============================== 3 passed in 7.56s ==============================