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fertilizer_api.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+import pandas as pd
+import joblib
+import os
+import logging
+from typing import Dict, List
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="Fertilizer Recommendation API")
+
+# Define model file paths
+PIPELINE_PATH = r"fertilizer_pipeline.joblib"
+ENCODER_PATH = r"fertilizer_label_encoder.joblib"
+
+# Valid crops
+VALID_CROPS = [
+    'Sugarcane', 'Jowar', 'Cotton', 'Rice', 'Wheat', 'Groundnut',
+    'Maize', 'Tur', 'Urad', 'Moong', 'Gram', 'Masoor', 'Soybean',
+    'Ginger', 'Turmeric', 'Grapes'
+]
+
+# Load pipeline and encoder at startup
+try:
+    pipeline = joblib.load(PIPELINE_PATH)
+    label_encoder = joblib.load(ENCODER_PATH)
+    logger.info("Pipeline and encoder loaded successfully")
+except Exception as e:
+    logger.error(f"Failed to load pipeline or encoder: {str(e)}")
+    raise Exception(f"Failed to load pipeline or encoder: {str(e)}")
+
+# Pydantic model for input validation
+class FertilizerInput(BaseModel):
+    Nitrogen: float = Field(..., ge=20, le=150, description="Nitrogen content in soil (kg/ha)")
+    Phosphorus: float = Field(..., ge=10, le=90, description="Phosphorus content in soil (kg/ha)")
+    Potassium: float = Field(..., ge=5, le=150, description="Potassium content in soil (kg/ha)")
+    pH: float = Field(..., ge=5.5, le=8.5, description="Soil pH value")
+    Rainfall: float = Field(..., ge=300, le=1700, description="Rainfall in millimeters")
+    Temperature: float = Field(..., ge=10, le=40, description="Temperature in Celsius")
+    Crop: str = Field(..., description="Crop type", enum=VALID_CROPS)
+
+# Synchronous prediction function
+def predict_fertilizer(input_data: Dict) -> Dict:
+    try:
+        # Convert input to DataFrame
+        input_df = pd.DataFrame([input_data])
+        
+        # Validate required columns
+        required_cols = ['Nitrogen', 'Phosphorus', 'Potassium', 'pH', 'Rainfall', 'Temperature', 'Crop']
+        missing_cols = set(required_cols) - set(input_df.columns)
+        if missing_cols:
+            raise ValueError(f"Missing required columns: {missing_cols}")
+        
+        # Predict
+        y_pred_encoded = pipeline.predict(input_df)
+        y_pred_label = label_encoder.inverse_transform(y_pred_encoded)[0]
+        
+        return {
+            "fertilizer": y_pred_label,
+            "status": "success"
+        }
+    except Exception as e:
+        logger.error(f"Prediction error: {str(e)}")
+        return {
+            "fertilizer": "",
+            "status": "failure",
+            "error": str(e)
+        }
+
+@app.post("/predict_fertilizer")
+async def predict_fertilizer_endpoint(input_data: FertilizerInput):
+    try:
+        # Check if files exist
+        for path in [PIPELINE_PATH, ENCODER_PATH]:
+            if not os.path.exists(path):
+                raise HTTPException(status_code=500, detail=f"File not found: {path}")
+        
+        # Convert Pydantic model to dict
+        input_dict = input_data.dict()
+        
+        # Make prediction
+        result = predict_fertilizer(input_dict)
+        
+        if result["status"] == "failure":
+            raise HTTPException(status_code=400, detail=result["error"])
+        
+        return result
+    
+    except Exception as e:
+        logger.error(f"Error processing prediction: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error processing prediction: {str(e)}")
+
+@app.get("/")
+async def root():
+    return {"message": "Fertilizer Recommendation API is running. Use /predict_fertilizer endpoint to send input data."}
+
+@app.get("/valid_inputs")
+async def get_valid_inputs():
+    return {
+        "Nitrogen": {"min": 20, "max": 150, "unit": "kg/ha"},
+        "Phosphorus": {"min": 10, "max": 90, "unit": "kg/ha"},
+        "Potassium": {"min": 5, "max": 150, "unit": "kg/ha"},
+        "pH": {"min": 5.5, "max": 8.5, "unit": "pH"},
+        "Rainfall": {"min": 300, "max": 1700, "unit": "mm"},
+        "Temperature": {"min": 10, "max": 40, "unit": "Celsius"},
+        "Crop": VALID_CROPS
+    }

fertilizer_label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0013e565006a0f4760832ae8cdc87934a1367d96c5fd64f37db595eaccb99b3
+size 866

fertilizer_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:20ac229f796c164db8dcfdbff6eb3bf9f9c25e7cd0d4ee0a5f6457f5f05484df
+size 46438490

requirements.txt

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+fastapi==0.112.2
+uvicorn==0.32.1
+pandas==2.2.3
+scikit-learn==1.6.1
+numpy==2.0.2
+joblib==1.4.2
+pydantic==2.10.3
+markupsafe==2.1.5
+anyio==4.7.0