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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 current directory contents into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /code/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
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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 ---
-title: Text Generation GPT2
-emoji: 💻
-colorFrom: indigo
-colorTo: gray
+title: Text Generation
+emoji: 🌍
+colorFrom: green
+colorTo: yellow
 sdk: docker
 pinned: false
 license: mit

app.py

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+# Install the necessary packages
+# pip install accelerate transformers fastapi pydantic torch
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from pydantic import BaseModel
+from fastapi import FastAPI
+# Import the required library
+from transformers import pipeline
+# Load the tokenizer and model
+
+# Initialize the FastAPI app
+app = FastAPI(docs_url="/")
+
+# Define the request model
+class RequestModel(BaseModel):
+    input: str
+
+# Define a greeting endpoint
+@app.get("/")
+def greet_json():
+    return {"message": "working..."}
+
+# Define the text generation endpoint
+@app.post("/generatetext")
+def get_response(request: RequestModel):
+    # Define the task and model
+	task = "text-generation"
+	model_name = "gpt2"
+
+	# Define the input text, maximum output length, and the number of return sequences
+	input_text = "he draw to the town "
+	max_output_length = 50
+	num_of_return_sequences = 1
+
+	# Initialize the text generation pipeline
+	text_generator = pipeline(
+		task,
+		model=model_name
+	)
+
+	# Generate text sequences
+	generated_texts = text_generator(
+		input_text,
+		max_length=max_output_length,
+		num_return_sequences=num_of_return_sequences
+	)
+
+
+    return {"generated_text": generated_texts}
+
+# To run the FastAPI app, use the command: uvicorn <filename>:app --reload

requirements.txt

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+fastapi==0.74.*
+requests==2.27.*
+uvicorn[standard]==0.17.*
+sentencepiece==0.1.*
+torch==1.11.*
+transformers==4.*