Add application file

Dockerfile

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+# Use the official Python image
+FROM python:3.11-slim
+
+# Set the working directory
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Expose the port FastAPI will run on
+EXPOSE 7860
+
+# Run the FastAPI app with Uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

app/lstm_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd738f5910dfcddf00835fafdedbbfd66df8fe1be433665ffa067259efe4d8fe
+size 427312

app/routes.py

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+from fastapi import FastAPI, HTTPException, APIRouter
+import pandas as pd
+import numpy as np
+from keras.models import load_model
+from pydantic import BaseModel
+
+# Load the model
+model = load_model("app/lstm_model.h5")
+# Define the FastAPI app
+router = APIRouter()
+
+class PredictionInput(BaseModel):
+    input_data: dict
+
+def preprocess_and_predict(input_data):
+    input_data = input_data.drop(columns=['COMPANY', 'PRICE OF LAST TRANSACTION'])
+    # Load the pre-trained model
+    timesteps = model.input_shape[1]
+    features = model.input_shape[2]
+
+    # Ensure input_data has the correct number of features
+    input_data = input_data.iloc[:, :features]
+
+    # Handle missing values and normalize
+    input_data = input_data.fillna(0)
+    max_value = input_data.max().max()
+    input_data_normalized = input_data / max_value
+
+    # Check if there are enough rows for timesteps
+    if len(input_data) < timesteps:
+        raise ValueError(f"Input data must have at least {timesteps} rows for prediction.")
+
+    # Reshape the data
+    input_data_reshaped = np.array([input_data_normalized.values[-timesteps:]])
+    input_data_reshaped = input_data_reshaped.reshape(1, timesteps, features)
+
+    # Predict
+    predictions = model.predict(input_data_reshaped)
+    predictions_denormalized = predictions * max_value
+
+    return predictions_denormalized.round()[0][0]
+
+# API endpoint
+@router.post("/predict/")
+async def predict(payload: PredictionInput):
+    try:
+        input_data = payload.input_data
+        dataframe = pd.DataFrame.from_dict(input_data)
+        
+        return {"prediction": preprocess_and_predict(input_data=dataframe)}
+
+    except ValueError as e:
+        raise HTTPException(status_code=422, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")

main.py

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+from fastapi import FastAPI
+from app.routes import router as api_router
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import JSONResponse
+import logging
+
+
+# Initialize the FastAPI app
+app = FastAPI(
+    title="API for the DAS Homework",
+    description="This api is is used to serve for the DAS Homework web application",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["https://das-prototype.web.app"],
+    allow_credentials=True,
+    allow_methods=["*"],  # Allow all HTTP methods (e.g., GET, POST)
+    allow_headers=["*"],  # Allow all headers
+)
+
+logging.basicConfig(level=logging.INFO)
+
+@app.middleware("http")
+async def log_requests(request: Request, call_next):
+    try:
+        body = await request.body()
+        logging.info(f"Request body: {body.decode()}")
+        response = await call_next(request)
+        logging.info(f"Response status: {response.status_code}")
+        return response
+    except Exception as e:
+        logging.error(f"Error occurred: {e}")
+        raise e
+
+# Include the API router
+app.include_router(api_router)
+
+@app.get("/")
+async def root():
+    return {"message": "API for the DAS Homework"}

requirements.txt

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+absl-py==2.1.0
+annotated-types==0.7.0
+anyio==4.8.0
+astunparse==1.6.3
+certifi==2024.12.14
+charset-normalizer==3.4.1
+click==8.1.8
+fastapi==0.115.6
+flatbuffers==24.12.23
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.69.0
+h11==0.14.0
+h5py==3.12.1
+idna==3.10
+keras==3.8.0
+libclang==18.1.1
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+mdurl==0.1.2
+ml-dtypes==0.4.1
+namex==0.0.8
+numpy==2.0.2
+opt_einsum==3.4.0
+optree==0.14.0
+packaging==24.2
+pandas==2.2.3
+protobuf==5.29.3
+pydantic==2.10.5
+pydantic_core==2.27.2
+Pygments==2.19.1
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2024.2
+requests==2.32.3
+rich==13.9.4
+six==1.17.0
+sniffio==1.3.1
+starlette==0.41.3
+tensorboard==2.18.0
+tensorboard-data-server==0.7.2
+tensorflow==2.18.0
+tensorflow-io-gcs-filesystem==0.37.1
+termcolor==2.5.0
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.3.0
+uvicorn==0.34.0
+Werkzeug==3.1.3
+wrapt==1.17.2