Update app.py

app.py

@@ -1,76 +1,77 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, Optional
-import pickle
-import numpy as np
-import pandas as pd
-import json
-
-app = FastAPI()
-
-# Load models
-with open("model_useing_Location_Seasion_Area.pkl", "rb") as f:
-    location_model = pickle.load(f)
-
-with open("model_useing_npk.pkl", "rb") as f:
-    npk_model = pickle.load(f)
-
-# Load unique values from the JSON file
-with open("unique_values.json", "r") as f:
-    unique_values = json.load(f)
-
-
-class CropRequest(BaseModel):
-    state: Optional[str] = None
-    district: Optional[str] = None
-    season: Optional[str] = None
-    area: Optional[float] = None
-    N: Optional[float] = None
-    P: Optional[float] = None
-    K: Optional[float] = None
-    temperature: Optional[float] = None
-    humidity: Optional[float] = None
-    ph: Optional[float] = None
-    rainfall: Optional[float] = None
-
-
-def get_top_recommendations(model, input_data, top_n=3):
-    if hasattr(model, "predict_proba"):
-        proba = model.predict_proba(input_data)
-        top_indices = np.argsort(proba[0])[-top_n:][::-1]
-        return [model.classes_[i] for i in top_indices]
-    return model.predict(input_data).tolist()
-
-
-@app.post("/predict")
-def predict_crop(request: CropRequest):
-    location_recommendations = []
-    npk_recommendations = []
-
-    if request.state and request.district and request.season and request.area:
-        try:
-            input_data = pd.DataFrame([[request.state, request.district, request.season, np.log1p(request.area)]],
-                                      columns=['State_Name', 'District_Name', 'Season', 'Area'])
-            location_recommendations = get_top_recommendations(location_model, input_data, top_n=3)
-        except ValueError:
-            pass  # Handle invalid input
-
-    if request.N is not None and request.P is not None and request.K is not None and request.temperature is not None \
-            and request.humidity is not None and request.ph is not None and request.rainfall is not None:
-        try:
-            input_data = np.array([[request.N, request.P, request.K, request.temperature,
-                                    request.humidity, request.ph, request.rainfall]])
-            npk_recommendations = get_top_recommendations(npk_model, input_data, top_n=3)
-        except ValueError:
-            pass  # Handle invalid input
-
-    location_lower = {crop.lower(): crop for crop in location_recommendations}
-    common_crops_lower = set(location_lower.keys()) & set(crop.lower() for crop in npk_recommendations)
-    common_crops = [location_lower[crop] for crop in common_crops_lower]
-
-    location_recommendations = [crop for crop in location_recommendations if crop.lower() not in common_crops_lower]
-    npk_recommendations = [crop for crop in npk_recommendations if crop.lower() not in common_crops_lower]
-
-    merged_crops = common_crops + npk_recommendations + location_recommendations
-    final_recommendations = [crop.lower() for crop in merged_crops[:4]]
-
-    return {"recommendations": final_recommendations if final_recommendations else []}
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+import pickle
+import numpy as np
+import pandas as pd
+import json
+
+app = FastAPI()
+
+# Load models
+with open("model_useing_Location_Seasion_Area.pkl", "rb") as f:
+    location_model = pickle.load(f)
+
+with open("model_useing_npk.pkl", "rb") as f:
+    npk_model = pickle.load(f)
+
+# Load unique values from the JSON file
+with open("unique_values.json", "r") as f:
+    unique_values = json.load(f)
+
+
+class CropRequest(BaseModel):
+    state: Optional[str] = None
+    district: Optional[str] = None
+    season: Optional[str] = None
+    area: Optional[float] = None
+    N: Optional[float] = None
+    P: Optional[float] = None
+    K: Optional[float] = None
+    temperature: Optional[float] = None
+    humidity: Optional[float] = None
+    ph: Optional[float] = None
+    rainfall: Optional[float] = None
+
+
+def get_top_recommendations(model, input_data, top_n=3):
+    if hasattr(model, "predict_proba"):
+        proba = model.predict_proba(input_data)
+        top_indices = np.argsort(proba[0])[-top_n:][::-1]
+        return [model.classes_[i] for i in top_indices]
+    return model.predict(input_data).tolist()
+
+
+@app.post("/predict")
+def predict_crop(request: CropRequest):
+    location_recommendations = []
+    npk_recommendations = []
+
+    if request.state and request.district and request.season and request.area:
+        try:
+            input_data = pd.DataFrame([[request.state, request.district, request.season, np.log1p(request.area)]],
+                                      columns=['State_Name', 'District_Name', 'Season', 'Area'])
+            location_recommendations = get_top_recommendations(location_model, input_data, top_n=3)
+        except ValueError:
+            pass  # Handle invalid input
+
+    if request.N is not None and request.P is not None and request.K is not None and request.temperature is not None \
+            and request.humidity is not None and request.ph is not None and request.rainfall is not None:
+        try:
+            input_data = np.array([[request.N, request.P, request.K, request.temperature,
+                                    request.humidity, request.ph, request.rainfall]])
+            npk_recommendations = get_top_recommendations(npk_model, input_data, top_n=3)
+        except ValueError:
+            pass  # Handle invalid input
+
+    location_lower = {crop.lower(): crop for crop in location_recommendations}
+    common_crops_lower = set(location_lower.keys()) & set(crop.lower() for crop in npk_recommendations)
+    common_crops = [location_lower[crop] for crop in common_crops_lower]
+
+    location_recommendations = [crop for crop in location_recommendations if crop.lower() not in common_crops_lower]
+    npk_recommendations = [crop for crop in npk_recommendations if crop.lower() not in common_crops_lower]
+
+    merged_crops = common_crops + npk_recommendations + location_recommendations
+    final_recommendations = [crop.lower() for crop in merged_crops[:4]]
+
+    return {"recommendations": final_recommendations if final_recommendations else []}