adding main

app.py

@@ -0,0 +1,97 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+import matplotlib
+import json
+import pickle as pck
+import joblib
+import pandas as pd
+
+#reading the data base for market information
+db = pd.read_csv('/content/database.csv')
+
+#loading the feature list
+with open('/content/features.json') as f:
+   features = json.load(f)
+
+#loading our encoder
+with open(r"/content/columnTransformer.pickle", "rb") as file:
+    encoder = pck.load(file)
+model = joblib.load("/content/model.joblib")
+
+months = {'January': 1.0, 'February': 2.0, 'March': 3.0, 'April': 4.0, 'May': 5.0, 'June': 6.0, 'July': 7.0, 'August': 8.0, 'September': 9.0, 'October': 10.0, 'November': 11.0, 'December': 12.0}
+
+def encode(region,division,market,category, commodity,  month):
+    encoded = encoder.transform([[months[month], region, division,market,category,commodity]])
+    return encoded
+
+def predict(inp, quantity):
+    pred = model.predict(inp.toarray())
+    pred = '{:.2f}'.format(abs(pred[0])*quantity)
+    return float(pred)
+
+
+def plot_fig(compared,market):
+    #ploting figure for comarison
+    compared.sort(key=lambda tup: tup[1])
+    mk_name = [ m_name[0] for m_name in compared]
+    prices =  [ price[1] for price in compared]
+    colors = ["red" if i == market else "blue" for i in mk_name]
+    fig = plt.figure(figsize=(15,9))
+    plt.bar(mk_name, prices,width=0.5,  color=colors)
+    plt.title('Price compared to other markets')
+    #plt.xlabel('Markets')
+    plt.xticks(rotation=45)
+    plt.ylabel('Price (Fcfa)')
+    return fig
+
+def compararator(region,division,market,category, commodity,  month, quantity):
+    #compare prediction for other marlkets
+    db_cop = db[db.market!= market]
+    samples = db_cop.sample(5)
+    compared = []
+    #predict for the given input
+    inp = encode(region,division,market,category, commodity,  month)
+    pred = predict(inp,quantity) 
+    compared.append((market,pred))
+
+    #predict for  5 random markets for price comparison
+    for index, sample in samples.iterrows():
+        inp = encode(sample['region'],sample['division'],sample['market'],category, commodity,  month)
+        ypred = predict(inp,quantity) 
+        compared.append((sample['market'],ypred))
+    fig = plot_fig(compared, market)
+    return pred, fig
+
+
+
+def evaluate(region,division,market,category, commodity,  month, quantity):
+    ypred,fig = compararator(region,division,market,category, commodity,  month, quantity)
+    out = str(ypred) + "Fcfa for " +str(quantity)+ " (KG/L/P)"
+    st = "Price of " + commodity + " in " + market + " is : " + out
+    
+    return st, fig 
+
+
+inputs = [
+    gr.Dropdown(features['regions'], label="Region"),
+    gr.Dropdown(features['division'], label="Division"),
+    gr.Dropdown(features['market'], label="market"),
+    gr.Dropdown(features['categories'], label="category"),
+    gr.Dropdown(features['commodity'], label="commodity"),
+    gr.Dropdown(list(months.keys()), label="Month"),
+    gr.Slider(1, 100, value=1, step=0.5, label= "quantity (Kilogram / Liters)", interactive=True),
+]
+outputs = ["label", gr.Plot(label="Prices in other markets").style(container=True)]
+desc= "Machine learning for food price prediction in Various Cameroon markets (Retail Price)"		
+camfreg = gr.Interface(
+    fn=evaluate,
+    inputs=inputs,
+    outputs=outputs,
+    cache_examples=True,
+    article='KG = kilogram, L = Liter, P = piece',
+     title= 'Camfreg : Cameroon food prices Prediction',
+     description= desc 
+)
+
+if __name__ == "__main__":
+    camfreg.launch(share=True)