test

Dockerfile

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+# Use the official Python 3.9 image
+FROM python:3.9
+
+# Set the working directory to /code
+WORKDIR /code
+
+# Copy the requirements.txt file into the container
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+# Expose the port FastAPI will run on
+EXPOSE 7860
+
+# Start the FastAPI app on port 7860, the default port expected by Spaces
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README copy.md

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+---
+title: Text Generation
+emoji: 🏆
+colorFrom: red
+colorTo: purple
+sdk: docker
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from transformers import pipeline
+
+# Create a new FastAPI app instance
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allow all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allow all methods
+    allow_headers=["*"],  # Allow all headers
+)
+
+# Initialize the text generation pipeline
+pipe = pipeline("text2text-generation", model="google/flan-t5-small")
+
+# Define a root path to verify the server is running
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the FastAPI application. Use /generate to generate text."}
+
+# Define a function to handle the GET request at `/generate`
+@app.get("/generate")
+def generate(text: str):
+    """
+    Using the text2text-generation pipeline from `transformers`, generate text
+    from the given input text. The model used is `google/flan-t5-small`.
+    """
+    # Use the pipeline to generate text from the given input text
+    output = pipe(text)
+    
+    # Return the generated text in a JSON response
+    return {"output": output[0]["generated_text"]}

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch