feat added bert application file

.gitignore

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+# Ignore Python cache files
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.pyo
+*.pdb
+*.egg-info/
+*.log
+
+# Ignore virtual environments
+venv/
+env/
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Ignore local Hugging Face cache (to avoid unnecessary large files)
+.huggingface/
+.cache/
+datasets/
+
+# Ignore Docker build artifacts
+*.dockerignore
+*.tar
+*.img
+*.iso
+.DS_Store
+
+# Ignore Hugging Face Space build files
+logs/
+output/
+tmp/
+config.json
+hf-token
+
+# Ignore system files
+Thumbs.db
+.DS_Store
+.idea/
+.vscode/

Dockerfile

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+FROM python:3.9
+
+# Create a non-root user for security
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+# Install dependencies
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy application files
+COPY --chown=user . /app
+
+# Run the FastAPI app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+
+app = FastAPI()
+
+sentiment_analyzer = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+class SentimentRequest(BaseModel):
+    text: str
+
+@app.post("/sentiment")
+async def analyze_sentiment(request: SentimentRequest):
+    result = sentiment_analyzer(request.text)
+    print(result)
+    return {"label": result[0]["label"], "score": result[0]["score"]}
+
+@app.get("/")
+def greet_json():
+    return {"message": "BERT Sentiment Analysis API is running!"}

requirements.txt

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+fastapi
+uvicorn[standard]
+torch
+transformers