feat: v1

.editorconfig

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+root = true
+
+[*]
+end_of_line = lf
+insert_final_newline = true
+
+[*.{js,json,yml}]
+charset = utf-8
+indent_style = space
+indent_size = 2

.env.example

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+# INFRA
+TRAEFIK_URL="proxy.qsar.localhost"
+TRAEFIK_BASIC_AUTH="admin:$$apr1$$gaS8tVEe$$MjqM8IlBvz2PRFEWcwha1/"
+UI_URL="qsar.localhost"
+PLAUSIBLE_URL="analytics.qsar.localhost"
+
+# API
+DATABASE_URL="postgresql://postgres:postgres@database:5432/qsar?schema=public"
+
+# ANALYTICS
+BASE_URL="https://${PLAUSIBLE_URL}"
+SECRET_KEY_BASE=""
+ENABLE_EMAIL_VERIFICATION=false
+
+# UI
+NEXT_PUBLIC_ANALYTICS_DOMAIN="${BASE_URL}"
+NEXT_PUBLIC_ANALYTICS_HOST="${UI_URL}"

.gitattributes

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+/.yarn/**            linguist-vendored
+/.yarn/releases/*    binary
+/.yarn/plugins/**/*  binary
+/.pnp.*              binary linguist-generated

.github/workflows/release.yml

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+name: Release
+
+on: workflow_dispatch
+
+permissions:
+  contents: write
+
+jobs:
+  release:
+    runs-on: ubuntu-20.04
+    steps:
+      - name: Checkout Source Code
+        uses: actions/checkout@v2
+
+      - name: Install Node
+        uses: actions/setup-node@v3
+        with:
+          node-version: 20
+
+      - name: Install Yarn
+        run: corepack enable |
+          corepack prepare yarn@3.6.4 --activate
+
+      # Yarn dependencies cannot be cached until yarn is installed
+      # WORKAROUND: https://github.com/actions/setup-node/issues/531
+      - name: Extract Cached Dependencies
+        uses: actions/setup-node@v3
+        with:
+          cache: yarn
+
+      - name: Install Dependencies
+        run: yarn --immutable
+
+      - name: Set Git user to Actions Bot
+        run: |
+          git config --local user.name 'github-actions[bot]'
+          git config --local user.email '41898282+github-actions[bot]@users.noreply.github.com'
+
+      - name: Run Release
+        run: yarn release-it --ci
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

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+.yarn
+files
+node_modules
+report.txt
+.env
+.plausible.env
+clickhouse
+__pycache__
+
+*.h5
+*.hdf5
+
+.yarn/*
+!.yarn/patches
+!.yarn/plugins
+!.yarn/releases
+!.yarn/sdks
+!.yarn/versions

.husky/commit-msg

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+#!/usr/bin/env sh
+. "$(dirname -- "$0")/_/husky.sh"
+
+yarn commitlint ${1}

.release-it.json

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+{
+  "git": {
+    "requireBranch": "main",
+    "commitMessage": "chore: release v${version}"
+  },
+  "github": {
+    "release": true
+  },
+  "plugins": {
+    "@release-it/bumper": {
+      "out": ["apps/ui/package.json", "packages/database/package.json"]
+    },
+    "@release-it/conventional-changelog": {
+      "infile": "CHANGELOG.md",
+      "preset": {
+        "name": "conventionalcommits"
+      }
+    }
+  }
+}

.vscode/settings.json

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+{
+  "editor.codeActionsOnSave": {
+    "source.fixAll": "explicit"
+  },
+  "[prisma]": {
+    "editor.formatOnSave": true,
+    "editor.defaultFormatter": "Prisma.prisma"
+  },
+  "cssVariables.lookupFiles": [
+    "**/*.css",
+    "**/*.scss",
+    "**/*.sass",
+    "**/*.less",
+    "node_modules/@mantine/core/esm/index.css"
+  ],
+  "python.languageServer": "Jedi"
+}

.yarnrc.yml

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+nodeLinker: node-modules

CHANGELOG.md

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+
+
+## 0.1.1 (2023-12-20)
+
+
+### Bug Fixes
+
+* added 'pEC50 = ' to the start of the equation ([7bf8867](https://github.com/LABIOQUIM/plasmoqsar/commit/7bf88675e882fa3fa9c4a09295ade678c2c2e6f3))
+
+## 0.1.0 (2023-12-20)
+
+
+### Features
+
+* added csv download, about page ([1bf73f1](https://github.com/LABIOQUIM/plasmoqsar/commit/1bf73f14e977bab82a04a29dae2bff3d9729d508))
+
+## 0.0.9 (2023-12-19)
+
+
+### Bug Fixes
+
+* remove user name initial from avatar circle ([d686329](https://github.com/LABIOQUIM/plasmoqsar/commit/d686329d1b3a2d00ba396e42853294fc2e7109b2))
+
+## 0.0.8 (2023-12-19)
+
+## 0.0.7 (2023-12-18)
+
+
+### Bug Fixes
+
+* wrong path redirects ([30d17af](https://github.com/LABIOQUIM/plasmoqsar/commit/30d17af9bf321c80a9f0a66b749ad3c0e4fd3200))
+
+## 0.0.6 (2023-12-18)
+
+## 0.0.5 (2023-12-18)
+
+
+### Bug Fixes
+
+* submission form not accepting more than 8kb ([e8e19fb](https://github.com/LABIOQUIM/plasmoqsar/commit/e8e19fb582fe11af0b726e73c2f1e997bc8ac41d))
+
+## 0.0.4 (2023-12-04)
+
+## 0.0.3 (2023-12-04)
+
+
+### Bug Fixes
+
+* added alert for user already existing on registration ([ea3fbd3](https://github.com/LABIOQUIM/qsar/commit/ea3fbd32240ca7756f1ff4f66b1c5057b51307c8))
+
+## 0.0.2 (2023-12-04)
+
+## 0.0.1 (2023-12-04)

README.md

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+# QSAR Model
+
+### Setting up
+This repository is a Monorepo, meaning all necessary code is here. You'll still need external tools to make it work.  
+1. Make sure you have [Docker](https://docker.com).
+1. You'll also need the [nfsvol plugin](https://github.com/cirocosta/nfsvol) installed.  
+1. Then you can copy the file `.env.example` to `.env`. You don't need to change anything at this time.
+
+#### Development
+1. On Hold for now.
+
+#### Production
+If you want to simply instantiate and use, this is probably what you want.
+1. Rewriting...
+
+If everything went okay, you should be able to go to [localhost:3000](http://localhost:3000), register a new user and start using the app.

apps/predictor/Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY ./app /code/app
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
+
+# If running behind a proxy like Nginx or Traefik add --proxy-headers
+# CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80", "--proxy-headers"]

apps/predictor/Dockerfile.dev

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

apps/predictor/app/main.py

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+from fastapi import FastAPI, UploadFile
+from contextlib import asynccontextmanager
+from app.metrics import Metrics
+from PIL import Image
+from io import BytesIO
+import numpy as np
+import tensorflow as tf
+
+model = {}
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global model
+    dependencies = {
+        'precision': Metrics.precision,
+        'recall': Metrics.recall,
+        'f1_score': Metrics.f1_score,
+        'specificity': Metrics.specificity,
+        'npv': Metrics.npv,
+        'mcc': Metrics.mcc
+    }
+    model = tf.keras.models.load_model('/code/app/ResNet101V2_weights1.hdf5', custom_objects=dependencies)
+    yield
+    model = "deu merda"
+
+
+app = FastAPI(lifespan=lifespan)
+
+
+@app.post("/")
+async def root(file: UploadFile):
+    contents = await file.read()
+    image = Image.open(BytesIO(contents))
+
+    img = image.resize((128, 128))
+
+    img = np.array(img) / 255.0
+    img = img[:, :, 0:3]
+    img = np.expand_dims(img, axis=0)
+
+    prediction = model.predict(img)
+    predicted = np.argmax(prediction, axis=1)
+
+    return int(predicted)
+

apps/predictor/app/metrics.py

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+import os
+import random
+from pathlib import Path
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from keras import backend as K
+
+class Metrics:
+
+    def recall(self, y_true, y_pred):
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+        recall_keras = true_positives / (possible_positives + K.epsilon())
+        return recall_keras
+
+
+    def precision(self, y_true, y_pred):
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+        precision_keras = true_positives / (predicted_positives + K.epsilon())
+        return precision_keras
+
+    def specificity(self, y_true, y_pred):
+        tn = K.sum(K.round(K.clip((1 - y_true) * (1 - y_pred), 0, 1)))
+        fp = K.sum(K.round(K.clip((1 - y_true) * y_pred, 0, 1)))
+        return tn / (tn + fp + K.epsilon())
+
+    def f1_score(self, y_true, y_pred):
+        p = self.precision(y_true, y_pred)
+        r = self.recall(y_true, y_pred)
+        return 2 * ((p * r) / (p + r + K.epsilon()))
+
+    # Netavie Predictive Error
+    def npv(self, y_true, y_pred):
+        tn = K.sum(K.round(K.clip((1 - y_true) * (1 - y_pred), 0, 1)))
+        fn = K.sum(K.round(K.clip(y_true * (1 - y_pred), 0, 1)))
+        return tn / (tn + fn + K.epsilon())
+
+    # Matthews Correlation_Coefficient
+    def mcc(self, y_true, y_pred):
+        tp = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        tn = K.sum(K.round(K.clip((1 - y_true) * (1 - y_pred), 0, 1)))
+        fp = K.sum(K.round(K.clip((1 - y_true) * y_pred, 0, 1)))
+        fn = K.sum(K.round(K.clip(y_true * (1 - y_pred), 0, 1)))
+
+        num = tp * tn - fp * fn
+        den = (tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)
+        return num / K.sqrt(den + K.epsilon())

apps/predictor/package.json

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+{
+  "name": "predictor",
+  "version": "0.0.0",
+  "packageManager": "yarn@3.7.0"
+}