Create app.py
Browse files
app.py
ADDED
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from fastapi import FastAPI
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from pydantic import BaseModel, Field
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from typing import Literal
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import json
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import numpy as np
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import onnxruntime as ort
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from typing_extensions import Annotated
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import gradio as gr
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from cryptography.fernet import Fernet
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import os
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# Model load
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key = os.getenv("ONNX_KEY")
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cipher = Fernet(key)
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VERSION = "0.0.1"
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TITLE = f"DVPI beregnings API (version {VERSION})"
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DESCRIPTION = "Beregn Dansk Vandløbs Plante Indeks (DVPI) fra dækningsgrad af plantearter. Beregningen er baseret på en model som efterligner DVPI beregningsmetoden og er dermed ikke eksakt, usikkerheden er i gennemsnit **±0.05 EQR-enheder**."
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URL = "https://huggingface.co/spaces/KennethTM/dvpi"
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# Load ONNX model and species mappings
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with open("model.bin", "rb") as f:
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encrypted = f.read()
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decrypted = cipher.decrypt(encrypted)
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ort_session = ort.InferenceSession(decrypted)
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with open("spec2idx.json", "r") as f:
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spec2idx = json.load(f)
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# Define types
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valid_species = tuple(spec2idx.keys())
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class SpeciesCover(BaseModel):
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species: dict[Literal[valid_species], Annotated[float, Field(ge=0, le=100)]]
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model_config = {
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"json_schema_extra": {
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"examples": [{
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"species": {
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"Potamogeton alpinus": 25.0,
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"Berula erecta": 15.5,
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"Calamagrostis canescens": 10.0
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}
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}]
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}
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}
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class EQRResult(BaseModel):
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EQR: float # Round to 2 decimals
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DVPI: int
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version: str = VERSION
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# Create FastAPI app
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app = FastAPI(title=TITLE,
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description=DESCRIPTION)
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def eqr_to_dvpi(eqr: float) -> int:
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if eqr < 0.20:
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return 1
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elif eqr < 0.35:
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return 2
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elif eqr < 0.50:
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return 3
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elif eqr < 0.70:
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return 4
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else:
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return 5
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# FastAPI routes
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@app.post("/dvpi")
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def predict(cover_data: SpeciesCover) -> EQRResult:
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"""Predict EQR and DVPI from species cover data"""
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# Initialize input vector with zeros
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input_vector = np.zeros((1, len(spec2idx)))
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print(cover_data.species)
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# Fill values from input
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for species, cover in cover_data.species.items():
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idx = spec2idx[species]
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input_vector[0, idx] = cover
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# Get prediction
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input_name = ort_session.get_inputs()[0].name
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ort_inputs = {input_name: input_vector.astype(np.float32)}
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ort_output = ort_session.run(None, ort_inputs)
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eqr = float(ort_output[0][0])
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dvpi = eqr_to_dvpi(eqr)
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return EQRResult(EQR=round(eqr, 2), DVPI=dvpi)
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@app.get("/arter")
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def list_species() -> dict:
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"""Return list of valid species names"""
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return {"species": list(spec2idx.keys())}
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# Gradio app
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def add_entry(species, cover, current_dict) -> tuple[SpeciesCover, str]:
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current_dict[species] = cover
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return current_dict, current_dict
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def gradio_predict(cover_data: dict):
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if len(cover_data) == 0:
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return {}
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data = SpeciesCover(species=cover_data)
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result = predict(data)
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return result.model_dump()
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with gr.Blocks() as io:
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gr.Markdown(f"# {TITLE}")
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gr.Markdown(DESCRIPTION)
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with gr.Tab(label = "Beregner"):
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gr.Markdown("Beregning er baseret på samfund af plantearter og deres dækningsgrad. Dækningsgraden angives i procent som summen af scoren for dækningsgraden (1-5) divideret med det samlede antal undersøgte kvadrater gange 5, og til sidste konverteret til procent. Eksempel: Potamogeton alpinus findes 3 felter med scorerne 2, 3 og 5 ud af 50 undersøgte kvadrater. Dækningsgraden for Potamogeton alpinus er derfor (2+3+5)/(50*5)*100 = 4%.")
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current_dict = gr.State({})
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with gr.Row():
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species_input = gr.Dropdown(choices=valid_species, label="Vælg art")
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cover_input = gr.Number(label="Dækningsgrad (%)", minimum=0, maximum=100)
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with gr.Row():
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add_btn = gr.Button("Tilføj")
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reset_btn = gr.Button("Nulstil")
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list_display = gr.JSON(label="Artsliste")
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calc_btn = gr.Button("Beregn")
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results = gr.JSON(label="Resultater")
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def reset_dict():
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return {}, {}, {}
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add_btn.click(
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add_entry,
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inputs=[species_input, cover_input, current_dict],
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outputs=[current_dict, list_display]
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)
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reset_btn.click(
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reset_dict,
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inputs=[],
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outputs=[current_dict, list_display, results]
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)
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calc_btn.click(
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gradio_predict,
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inputs=[current_dict],
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outputs=results
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)
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gr.Markdown("App og model af Kenneth Thorø Martinsen.")
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with gr.Tab(label="Dokumentation"):
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# Add markdown description with code to call the api in python
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gr.Markdown("## Eksempel på brug af API")
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gr.Markdown(f"API dokumentation kan findes på [{URL}/docs]({URL}/docs)")
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gr.Markdown("### Python")
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gr.Code(f"""
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import requests
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import json
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172 |
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173 |
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data = {{
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174 |
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"species": {{
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175 |
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"Potamogeton alpinus": 25.0,
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"Berula erecta": 15.5,
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177 |
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"Calamagrostis canescens": 10.0
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}}
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}}
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180 |
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response = requests.post("{URL}/dvpi", json=data)
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print(response.json())
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""")
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gr.Markdown("### R")
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186 |
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gr.Code(f"""
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library(httr)
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188 |
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library(jsonlite)
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189 |
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190 |
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data <- list(species = list(
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"Potamogeton alpinus" = 25.0,
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"Berula erecta" = 15.5,
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"Calamagrostis canescens" = 10.0
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))
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195 |
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response <- POST("{URL}/dvpi",
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body = toJSON(data, auto_unbox = TRUE),
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content_type("application/json"))
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199 |
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print(fromJSON(rawToChar(response$content)))
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""")
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# Mount Gradio app
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app = gr.mount_gradio_app(app, io, path="/")
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