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app.py

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+import gradio as gr
+from PIL import Image
+import requests
+import hopsworks
+import joblib
+import pandas as pd
+import numpy as np
+
+project = hopsworks.login(project='suyiw000')
+
+fs = project.get_feature_store()
+
+
+mf = project.get_model_registry()
+model = mf.get_model("food_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/food_model.pkl")
+print("Model downloaded")
+
+
+market = ['Badakhshan', 'Badghis', 'Baghlan', 'Balkh', 'Bamyan', 'Daykundi', 'Farah', 'Faryab', 'Ghazni', 'Ghor', 'Hilmand', 'Hirat', 'Jawzjan' 'Kabul', 'Kandahar', 'Kapisa', 'Khost', 'Kunar', 'Kunduz', 'Laghman', 'Logar', 'Maidan Wardak', 'Nangarhar', 'Nimroz', 'Nuristan', 'Paktika', 'Paktya', 'Panjsher', 'Parwan', 'Samangan', 'Sar-e-Pul', 'Takhar', 'Uruzgan', 'Zabul']
+commodity = ['Bread', 'Oil_cooking', 'Pulses', 'Rice_high', 'Rice_low', 'Salt', 'Sugar', 'Wheat', 'Wheatflour_high', 'Wheatflour_low']
+
+
+def predict_price(year, month, market, food):
+  market_empty = np.zeros(34)
+  market_name = ['Badakhshan', 'Badghis', 'Baghlan', 'Balkh', 'Bamyan', 'Daykundi', 'Farah', 'Faryab', 'Ghazni', 'Ghor', 'Hilmand', 'Hirat', 'Jawzjan' 'Kabul', 'Kandahar', 'Kapisa', 'Khost', 'Kunar', 'Kunduz', 'Laghman', 'Logar', 'Maidan Wardak', 'Nangarhar', 'Nimroz', 'Nuristan', 'Paktika', 'Paktya', 'Panjsher', 'Parwan', 'Samangan', 'Sar-e-Pul', 'Takhar', 'Uruzgan', 'Zabul']
+  market = []
+  for i in range(34):
+    temp_market = market_empty.copy()
+    temp_market[i] = 1.0
+    market.append(temp_market)
+
+  commodity_empty = np.zeros(10)
+  commodity_name = ['Bread', 'Oil_cooking', 'Pulses', 'Rice_high', 'Rice_low', 'Salt', 'Sugar', 'Wheat', 'Wheatflour_high', 'Wheatflour_low']
+  commodity=[]
+  for i in range(10):
+    commodity_array = commodity_empty.copy()
+    commodity_array[i] = 1.0
+    commodity.append(commodity_array)
+
+  commodity_with_names = dict(zip(commodity_name, commodity))
+  arrays_with_names = dict(zip(market_name, market))
+
+  date = ((year*10000+month*100+15)-20200000)/100000
+
+  input_data = np.concatenate([arrays_with_names[market], commodity_with_names[food], [date]]).reshape(1, -1)
+ 
+  prediction = model.predict(input_data)
+
+  return prediction
+
+
+demo = gr.Interface(
+    fn = predict_price,
+    title = "AFG FOOD PRICE PREDICTION",
+    allow_flagging="never",
+    inputs=[
+        gr.Number(label="Year"),
+        gr.Number(label="Mouth"),
+        gr.Dropdown(choices=market, label="Market"),
+        gr.Dropdown(choices=commodity, label="Food Type")
+        ],
+    outputs="text"
+)
+demo.launch(debug=True)

requirements.txt

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+hopsworks
+joblib
+scikit-learn