Update app.py

app.py

@@ -1,133 +1,45 @@
-import gradio as gr
 from fastapi import FastAPI
-import pandas as pd
-import numpy as np
-import os
+from pydantic import BaseModel
 import joblib
-import warnings
-warnings.filterwarnings('ignore')
-
-app = FastAPI(title="Crop Yield Predictor API")
-
-# ======== MODEL LOADING WITH JOBLIB ========
-def load_model_properly():
-    model_path = 'CropYieldPredictor_COMPATIBLE.joblib'
-    
-    if not os.path.exists(model_path):
-        return None, f"❌ Model file '{model_path}' not found!"
-    
-    try:
-        model = joblib.load(model_path)
-        return model, "✅ Model loaded successfully with joblib!"
-    except Exception as e:
-        return None, f"❌ Loading failed: {str(e)}"
-
-model, load_status = load_model_properly()
-print(load_status)
+import pandas as pd
+import uvicorn
 
-# ======== AVAILABLE AREAS ========
-AVAILABLE_AREAS = [
-    'Albania', 'Algeria', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria', 
-    'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Belarus', 'Belgium', 'Botswana', 
-    'Brazil', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cameroon', 'Canada', 
-    'Central African Republic', 'Chile', 'Colombia', 'Croatia', 'Denmark', 
-    'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Eritrea', 'Estonia', 
-    'Finland', 'France', 'Germany', 'Ghana', 'Greece', 'Guatemala', 'Guinea', 
-    'Guyana', 'Haiti', 'Honduras', 'Hungary', 'India', 'Indonesia', 'Iraq', 
-    'Ireland', 'Italy', 'Jamaica', 'Japan', 'Kazakhstan', 'Kenya', 'Latvia', 
-    'Lebanon', 'Lesotho', 'Libya', 'Lithuania', 'Madagascar', 'Malawi', 'Malaysia', 
-    'Mali', 'Mauritania', 'Mauritius', 'Mexico', 'Montenegro', 'Morocco', 
-    'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 
-    'Niger', 'Norway', 'Pakistan', 'Papua New Guinea', 'Peru', 'Poland', 'Portugal', 
-    'Qatar', 'Romania', 'Rwanda', 'Saudi Arabia', 'Senegal', 'Slovenia', 
-    'South Africa', 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Sweden', 
-    'Switzerland', 'Tajikistan', 'Thailand', 'Tunisia', 'Turkey', 'Uganda', 
-    'Ukraine', 'United Kingdom', 'Uruguay', 'Zambia', 'Zimbabwe'
-]
+# ✅ Initialize FastAPI app
+app = FastAPI(title="Crop Yield Predictor API", version="1.0")
 
-# ======== PREDICTION FUNCTION ========
-def predict_yield(area, item, year, rainfall, pesticides, temperature):
-    if model is None:
-        return f"❌ ERROR: {load_status}"
-    
-    try:
-        year_val = int(year)
-        rainfall_val = float(rainfall)
-        pesticides_val = float(pesticides)
-        temperature_val = float(temperature)
-        
-        input_data = {
-            'Area': [area],
-            'Item': [item],
-            'Year': [year_val],
-            'average_rain_fall_mm_per_year': [rainfall_val],
-            'pesticides_tonnes': [pesticides_val],
-            'avg_temp': [temperature_val]
-        }
-        
-        input_df = pd.DataFrame(input_data)
-        prediction = model.predict(input_df)
-        predicted_yield = prediction[0]
-        predicted_yield_kg_ha = predicted_yield * 0.1
-        
-        result_text = f"**🌾 Predicted Yield: {predicted_yield_kg_ha:,.0f} kg/ha**\n*({predicted_yield:,.0f} hg/ha)*"
-        
-        return result_text
-        
-    except Exception as e:
-        return f"❌ PREDICTION ERROR: {str(e)}"
+# ✅ Load your trained model
+model = joblib.load("CropYeildPredictor.pkl")
 
-# ======== GRADIO INTERFACE ========
-with gr.Blocks(title="Crop Yield Predictor", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🌾 Crop Yield Predictor API")
-    gr.Markdown(f"**Model Status:** {load_status}")
-    
-    with gr.Row():
-        with gr.Column():
-            area = gr.Dropdown(label="🌍 Country/Area", choices=AVAILABLE_AREAS, value="India")
-            item = gr.Textbox(label="🌱 Crop Type", value="Maize")
-            year = gr.Number(label="📅 Year", value=2023)
-            rainfall = gr.Textbox(label="💧 Average Rainfall (mm/year)", value="800.0")
-            pesticides = gr.Textbox(label="🧴 Pesticides (tonnes)", value="5000.0")
-            temperature = gr.Textbox(label="🌡️ Average Temperature (°C)", value="20.0")
-            predict_btn = gr.Button("🚀 Predict Yield", variant="primary")
-        
-        with gr.Column():
-            output = gr.Markdown(label="Prediction Result")
-    
-    predict_btn.click(
-        fn=predict_yield,
-        inputs=[area, item, year, rainfall, pesticides, temperature],
-        outputs=[output]
-    )
+# ✅ Define the expected input schema
+class CropInput(BaseModel):
+    country: str
+    crop_type: str
+    year: int
+    avg_rainfall: float
+    pesticides: float
 
-# ======== FASTAPI ROUTES ========
+# ✅ Home route
 @app.get("/")
-async def home():
-    return {"message": "Crop Yield Predictor API", "status": "running"}
-
-@app.get("/health")
-async def health():
-    return {"model_loaded": model is not None, "status": load_status}
-
-@app.get("/api/areas")
-async def get_areas():
-    return {"areas": AVAILABLE_AREAS}
-
-@app.post("/api/predict")
-async def api_predict(area: str, item: str, year: int, rainfall: float, pesticides: float, temperature: float):
-    result = predict_yield(area, item, year, rainfall, pesticides, temperature)
-    return {
-        "success": True if "❌" not in result else False,
-        "prediction": result,
-        "input": {
-            "area": area,
-            "item": item, 
-            "year": year,
-            "rainfall": rainfall,
-            "pesticides": pesticides,
-            "temperature": temperature
-        }
-    }
-
-app = gr.mount_gradio_app(app, demo, path="/")
+def home():
+    return {"message": "Crop Yield Predictor API is running successfully!"}
+
+# ✅ Prediction route
+@app.post("/predict")
+def predict_yield(data: CropInput):
+    # Convert input to DataFrame (must match training features order)
+    input_df = pd.DataFrame([{
+        "Country": data.country,
+        "Crop": data.crop_type,
+        "Year": data.year,
+        "average_rain_fall_mm_per_year": data.avg_rainfall,
+        "pesticides_tonnes": data.pesticides
+    }])
+
+    # Make prediction
+    prediction = model.predict(input_df)[0]
+
+    return {"predicted_yield": float(prediction)}
+
+# ✅ For local testing (won’t be used in HF)
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)