backend add

Pest-Disease-Prediction-Backend/.gitignore

+__pycache__/
+*.pyc
+
+myenv/

Pest-Disease-Prediction-Backend/app.py

+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import numpy as np
+import os
+import pickle
+
+# Load your machine learning model
+current_directory = os.getcwd()
+model_path = "./models/pest_disease.pickle"  # Replace with the path to your model
+with open(model_path, 'rb') as model_file:
+    pest_disease = pickle.load(model_file)
+
+# Create a FastAPI app
+app = FastAPI()
+
+# Define input and output data models
+class InputData(BaseModel):
+    temperature: float
+    humidity: float
+    wind_speed: float
+    disease_num: int
+    rain: int
+
+class OutputData(BaseModel):
+    status: str
+
+# Function to map status_num to descriptive labels
+def get_status_label(status_num):
+    if status_num == 1:
+        return "high"
+    elif status_num == 2:
+        return "medium"
+    elif status_num == 3:
+        return "low"
+    else:
+        return "unknown"
+@app.get("/")
+def read_root():
+    return {"message": "Hello, FastAPI!"}
+
+# Create a prediction endpoint for pest and disease model
+@app.post("/pest-disease", response_model=OutputData)
+async def predict_pest_disease(input_data: InputData):
+    input_values = np.array([input_data.temperature, input_data.humidity, input_data.wind_speed, input_data.disease_num, input_data.rain]).reshape(1, -1)
+    prediction = pest_disease.predict(input_values)
+    status_label = get_status_label(int(prediction))
+    return {"status": status_label}

Pest-Disease-Prediction-Backend/gunicorn_config.py

+workers = 4  # Adjust this value based on your server's resources
+bind = "0.0.0.0:8000"  # Bind to all available network interfaces
+
+# Optionally, set a log file
+accesslog = "/path/to/access.log"
+errorlog = "/path/to/error.log"

Pest-Disease-Prediction-Backend/requirements.txt

 annotated-types==0.5.0