DOCKERFILEEEEEEEEEE

Dockerfile

@@ -1,28 +1,19 @@
 # Use an official Python runtime as a base image
-FROM python:3.9-slim
+FROM python:3.9
 
-# Prevent Python from writing .pyc files and enable unbuffered logging
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV PYTHONUNBUFFERED=1
+WORKDIR /code
 
-# Set HF_HOME to a writable cache directory
-ENV HF_HOME=/app/.cache/huggingface
+COPY backend/requirements.txt /code/backend/requirements.txt
 
-# Create the cache directory and ensure it is writable
-RUN mkdir -p /app/.cache/huggingface && chmod -R 777 /app/.cache/huggingface
+RUN pip install --no-cache-dir --upgrade -r /code/backend/requirements.txt
 
-# Set the working directory inside the container
-WORKDIR /app
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user
+ENV PATH=/home/user/.local/bin:$PATH
 
-# Copy and install backend dependencies
-COPY backend/requirements.txt /app/backend/requirements.txt
-RUN pip install --upgrade pip && pip install -r /app/backend/requirements.txt
+WORKDIR $HOME/app
 
-# Copy the entire project into the container
-COPY . /app
+COPY --chown=user . $HOME/app
 
-# Expose port 8000 (the port our app will run on)
-EXPOSE 7680
-
-# Run the FastAPI app using Uvicorn
-CMD ["uvicorn", "backend.app:app", "--host", "0.0.0.0", "--port", "7680"]
+CMD ["uvicorn", "backend.app:app", "--host", "0.0.0.0", "--port", "7860"]

backend/app.py

@@ -1,7 +1,7 @@
 from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
-from transformers import GPT2Tokenizer, GPT2Model
+from transformers import GPT2Tokenizer, GPT2Model, pipeline
 import torch as t
 import logging
 
@@ -62,3 +62,26 @@ async def process_text(text: str):
     except Exception as e:
         logger.error(f"Error processing text: {e}")
         raise HTTPException(status_code=500, detail=str(e))
+
+# Initialize the text generation pipeline
+# This function will be able to generate text
+# given an input.
+pipe = pipeline("text2text-generation",
+model="google/flan-t5-small")
+
+# Define a function to handle the GET request at `/generate`
+# The generate() function is defined as a FastAPI route that takes a
+# string parameter called text. The function generates text based on the # input using the pipeline() object, and returns a JSON response
+# containing the generated text under the key "output"
+@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`, which
+    can be found [here](<https://huggingface.co/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"]}