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Browse filesuploading code v1
- Dockerfile +27 -0
- README.md +4 -3
- app.py +20 -0
- index/config +0 -0
- index/documents +0 -0
- index/embeddings +0 -0
- main.py +50 -0
- requirements.txt +5 -0
Dockerfile
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# Use the official Python 3.9 image
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FROM python:3.9
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# Set the working directory to /code
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WORKDIR /code
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# Copy the current directory contents into the container at /code
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COPY ./requirements.txt /code/requirements.txt
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# Install requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set home to the user's home directory
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
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README.md
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---
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title:
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emoji:
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colorFrom:
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colorTo: yellow
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sdk: docker
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Text Generation
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emoji: 🌍
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colorFrom: green
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colorTo: yellow
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sdk: docker
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from fastapi import FastAPI
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from transformers import pipeline
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# NOTE - we configure docs_url to serve the interactive Docs at the root path
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# of the app. This way, we can use the docs as a landing page for the app on Spaces.
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app = FastAPI(docs_url="/")
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pipe = pipeline("text2text-generation", model="google/flan-t5-small")
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@app.get("/generate")
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def generate(text: str):
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"""
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Using the text2text-generation pipeline from `transformers`, generate text
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from the given input text. The model used is `google/flan-t5-small`, which
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can be found [here](https://huggingface.co/google/flan-t5-small).
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"""
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output = pipe(text)
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return {"output": output[0]["generated_text"]}
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index/config
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Binary file (288 Bytes). View file
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index/documents
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Binary file (41 kB). View file
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index/embeddings
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Binary file (29.4 kB). View file
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main.py
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from fastapi import FastAPI
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# from transformers import pipeline
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from txtai.embeddings import Embeddings
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from txtai.pipeline import Extractor
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# NOTE - we configure docs_url to serve the interactive Docs at the root path
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# of the app. This way, we can use the docs as a landing page for the app on Spaces.
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app = FastAPI(docs_url="/")
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# Create embeddings model with content support
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embeddings = Embeddings({"path": "sentence-transformers/all-MiniLM-L6-v2", "content": True})
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embeddings.load('index')
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# Create extractor instance
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extractor = Extractor(embeddings, "google/flan-t5-base")
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pipe = pipeline("text2text-generation", model="google/flan-t5-large")
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@app.get("/generate")
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def generate(text: str):
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"""
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Using the text2text-generation pipeline from `transformers`, generate text
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from the given input text. The model used is `google/flan-t5-small`, which
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can be found [here](https://huggingface.co/google/flan-t5-small).
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"""
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output = pipe(text)
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return {"output": output[0]["generated_text"]}
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def prompt(question):
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return f"""Answer the following question using only the context below. Say 'no answer' when the question can't be answered.
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Question: {question}
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Context: """
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def search(query, question=None):
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# Default question to query if empty
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if not question:
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question = query
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return extractor([("answer", query, prompt(question), False)])[0][1]
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@app.get("/rag")
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def rag(question: str):
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# question = "what is the document about?"
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answer = search(question)
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# print(question, answer)
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return {answer}
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requirements.txt
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fastapi==0.74.*
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requests==2.27.*
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uvicorn[standard]==0.17.*
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sentencepiece==0.1.*
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txtai==6.0.*
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