migrating project

app.py

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+import gradio
+import cv2
+from sklearn.naive_bayes import BernoulliNB 
+import pickle
+import numpy as np
+
+# multiclass_model =  pickle.load(open('models/MulticlassModel_200x200', 'rb'))
+ensemble_model =  pickle.load(open('models/EnsembleModels_200x200', 'rb'))
+
+examples = ["images/Conso.jpg", "images/Incom.jpg"]
+
+def preprocess(img):
+    img = cv2.resize(img, (200,200))
+    img = cv2.adaptiveThreshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
+    img = np.reshape(img, (1,200*200))/255
+    return img
+
+def predict(img):
+    img = preprocess(img)
+    categories = {
+    "Inco": 2, 
+    "Teac": 1, 
+    "Cons": 0, 
+    "Publ": 4, 
+    "Econ": 3,
+    "Reaf": 5}
+
+    proba = np.zeros((6))
+    for key in categories.keys():
+        proba[categories[key]] = ensemble_model[key].predict_proba(img)[:,0]
+
+    return proba
+
+def generate_results(proba):
+    categories = [
+        "DLC",
+        "TLF", 
+        "IDR", 
+        "EHD",
+        "PLSF", 
+        "REAF",
+        "UNKNOWN"] 
+
+    scores = [0,0,0,0,0,0,0]
+    
+    choice = np.where(proba == np.amin(proba))[0]
+
+    if len(choice)>1:
+            choice = 6
+    scores[int(choice)] = 1
+
+    results = dict(zip(categories, scores))
+    return results
+
+def inference(img):
+    proba = predict(img)
+    results = generate_results(proba)
+    return results
+
+demo = gradio.Interface(
+    fn=inference,
+    inputs=gradio.Image(),
+    outputs=gradio.Label(),
+    title='Document Classification', 
+    description='Loan Document Classification Using A Naive Bayes Classifier Ensemble',
+    article='This demo was built as part of demo for a student project.\n\n\n#BuiltAtMetis',    
+    examples=examples)  
+
+demo.launch()

requirements.txt

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+opencv-python
+sklearn