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Dockerfile

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+### use the official python3.12 image
+FROM python:3.12
+
+# 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 any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir  --upgrade-r /code/requirements.txt
+
+## set up a new user
+RUN useradd user
+#switch to the new user
+USER user
+
+# set the home to 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 $HOMR/app (home app folder) setting the owner to the new user
+COPY --chown=user . $HOME/app
+
+# Start the FASTAPI App on port 7680
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7680"]

app.py

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+#text generation genai  app (text to text)
+from fastapi import FastAPI
+from transformers import pipeline
+# we used pipeline so we can call any  llm models that are  present in hugging face
+# creaye fastapi app instnace
+
+app = FastAPI()
+
+# initiliaze text generation pipeline
+# Use a pipeline as a high-level helper
+
+pipe = pipeline("text2text-generation", model="google/flan-t5-small")
+
+# create 
+@app.get("/")
+def home():
+    return {"message": "Welcome to the Text Generation App"}
+# generate fucntion to handle some get request /generate
+
+@app.get("/generate")
+def generate(text: str):              #whatever the text we give it will post to my pipeline
+    # use the pipeline to generate the text from given input text "(text:str)"
+    output = pipe(text)
+    return {"output": output[0]['generated_text']}           # outpu is given in the form of key  anf rom the output we will getting the response in the form  of list and over there

requirements.txt

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+fastapi
+requests
+uvicorn
+sentencepiece
+torch
+transformers