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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", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1,11 @@
 ---
-title: Rag Test Venkat
-emoji: 😻
-colorFrom: red
+title: Text Generation
+emoji: 🌍
+colorFrom: green
 colorTo: yellow
 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 transformers import pipeline
+
+
+# NOTE - we configure docs_url to serve the interactive Docs at the root path
+# of the app. This way, we can use the docs as a landing page for the app on Spaces.
+app = FastAPI(docs_url="/")
+
+pipe = pipeline("text2text-generation", model="google/flan-t5-small")
+
+
+@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).
+    """
+    output = pipe(text)
+    return {"output": output[0]["generated_text"]}

main.py

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+from fastapi import FastAPI
+# from transformers import pipeline
+from txtai.embeddings import Embeddings
+from txtai.pipeline import Extractor
+
+# NOTE - we configure docs_url to serve the interactive Docs at the root path
+# of the app. This way, we can use the docs as a landing page for the app on Spaces.
+app = FastAPI(docs_url="/")
+
+# Create embeddings model with content support
+embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2", "content": True})
+embeddings.load('index')
+
+# Create extractor instance
+extractor = Extractor(embeddings, "google/flan-t5-base")
+
+pipe = pipeline("text2text-generation", model="google/flan-t5-large")
+
+
+@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).
+    """
+    output = pipe(text)
+    return {"output": output[0]["generated_text"]}
+
+
+def prompt(question):
+  return f"""Answer the following question using only the context below. Say 'no answer' when the question can't be answered.
+            Question: {question}
+            Context: """
+
+
+def search(query, question=None):
+  # Default question to query if empty
+  if not question:
+    question = query
+
+  return extractor([("answer", query, prompt(question), False)])[0][1]
+
+
+@app.get("/rag")
+def rag(question: str):
+    # question = "what is the document about?"
+    answer = search(question)
+    # print(question, answer)
+    return {answer}

requirements.txt

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+fastapi==0.74.*
+requests==2.27.*
+uvicorn[standard]==0.17.*
+sentencepiece==0.1.*
+txtai==6.0.*