Fasika
commited on
Commit
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ede4edf
1
Parent(s):
eebd998
zero shot
Browse files- app.py +32 -18
- requirements.txt +2 -1
app.py
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from fastapi import FastAPI
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import
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from
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model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
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sequences = ["I've been waiting for a HuggingFace course my whole life.", "So have I!"]
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tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
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# Perform inference without gradient tracking
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with torch.no_grad():
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output = model(**tokens)
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# Convert logits to a list for JSON serialization
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logits = output.logits.tolist()
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app = FastAPI()
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@app.get("/")
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def greet_json():
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel, Field
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from typing import List
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from transformers import pipeline
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# Initialize the zero-shot classification pipeline
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classifier = pipeline("zero-shot-classification")
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# Define the FastAPI application
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app = FastAPI()
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# Pydantic model for input validation
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class ClassificationRequest(BaseModel):
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text: str = Field(..., example="This is a course about the Transformers library")
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labels: List[str] = Field(..., example=["education", "politics", "technology"])
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@app.get("/")
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def greet_json():
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"""
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A simple GET endpoint that returns a greeting message.
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"""
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return {"Hello": "World!"}
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@app.post("/classify")
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def zero_shot_classification(request: ClassificationRequest):
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"""
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A POST endpoint that performs zero-shot classification on the input text
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using the provided candidate labels.
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"""
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try:
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# Perform zero-shot classification
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result = classifier(
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request.text,
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candidate_labels=request.labels
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)
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return result
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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requirements.txt
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fastapi
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uvicorn[standard]
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torch
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transformers
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fastapi
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uvicorn[standard]
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torch
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transformers
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pydantic
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