ArxAlfa
commited on
Commit
•
a650593
1
Parent(s):
9d63bd4
Add model.joblib, update Dockerfile and
Browse files- Dockerfile +1 -1
- app.py +64 -5
- dataset/housing_price_dataset.csv +0 -0
- model.joblib +0 -0
- requirements.txt +30 -6
Dockerfile
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@@ -1,5 +1,5 @@
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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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# Use the official Python 3.9 image
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FROM python:3.9-slim
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# Set the working directory to /code
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WORKDIR /code
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app.py
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from fastapi import FastAPI
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# Create a new FastAPI app instance
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app = FastAPI(docs_url="/", redoc_url="/new_redoc")
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# Create a POST endpoint
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@app.get("/generate")
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def generate(
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from fastapi import FastAPI, UploadFile, File
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import numpy as np
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from sklearn.neural_network import MLPRegressor
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from sklearn.model_selection import KFold
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from sklearn.metrics import mean_squared_error
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import csv
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import io
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from joblib import load, dump
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# Load the model
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model = load("model.joblib")
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# Create a new FastAPI app instance
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app = FastAPI(docs_url="/", redoc_url="/new_redoc")
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# Create a POST endpoint
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@app.get("/generate/{squareFeet}/{bedrooms}/{bathrooms}/{yearBuilt}")
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def generate(
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squareFeet: float,
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bedrooms: float,
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bathrooms: float,
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yearBuilt: float,
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):
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global model
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prediction = model.predict([[squareFeet, bedrooms, bathrooms, yearBuilt]])
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return {"output": prediction[0]}
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@app.post("/train")
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async def train(file: UploadFile = File(...)):
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global model
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contents = await file.read()
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data = list(csv.reader(io.StringIO(contents.decode("utf-8"))))
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data_np = np.array(data[1:], dtype=object)
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# Delete the fourth column
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data_np = np.delete(data_np, 3, axis=1)
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data_np = np.array(data_np, dtype=float)
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# All columns except the last
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X = data_np[:, :-1]
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# Only the last column
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y = data_np[:, -1]
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y = np.ravel(y)
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# Fit the model
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kf = KFold(n_splits=4)
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accuracies = []
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for train_index, test_index in kf.split(X):
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X_train, X_test = X[train_index], X[test_index]
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y_train, y_test = y[train_index], y[test_index]
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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rmse = np.sqrt(mean_squared_error(y_test, predictions))
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accuracies.append(rmse)
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average_rmse = sum(accuracies) / len(accuracies)
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print(f"Average RMSE: {average_rmse}")
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dump(model, "model.joblib")
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return {"filename": file.filename, "average_rmse": average_rmse}
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dataset/housing_price_dataset.csv
ADDED
The diff for this file is too large to render.
See raw diff
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model.joblib
ADDED
Binary file (8.19 kB). View file
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requirements.txt
CHANGED
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# requirements.txt
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anyio==3.7.1
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asgiref==3.7.2
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certifi==2023.11.17
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charset-normalizer==2.0.12
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click==8.1.7
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fastapi==0.74.1
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h11==0.14.0
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httptools==0.6.1
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idna==3.6
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joblib==1.3.2
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numpy==1.26.2
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pydantic==1.10.13
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python-dotenv==1.0.0
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python-multipart==0.0.6
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PyYAML==6.0.1
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requests==2.27.1
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scikit-learn==1.3.2
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scipy==1.11.4
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sentencepiece==0.1.99
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sniffio==1.3.0
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starlette==0.17.1
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threadpoolctl==3.2.0
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typing_extensions==4.8.0
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urllib3==1.26.18
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uvicorn==0.17.6
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uvloop==0.19.0
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watchgod==0.8.2
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websockets==12.0
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