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diff --git a/‎ClassExamples/PJ128_AIML6m_Jan21/AI-flask-heroku/app.py
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diff --git a/‎ClassExamples/PJ128_AIML6m_Jan21/AI-flask-heroku/model.pkl
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diff --git a/‎ClassExamples/PJ128_AIML6m_Jan21/AI-flask-heroku/requirements.txt
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diff --git a/‎ClassExamples/PJ128_AIML6m_Jan21/TestAImodel_pickleLoad_test/logit_model.pkl
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diff --git a/‎ClassExamples/PJ128_AIML6m_Jan21/TestAImodel_pickleLoad_test/logit_modelp.pkl
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diff --git a/‎ClassExamples/PJ128_AIML6m_Jan21/TestAImodel_pickleLoad_test/test.py
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+web: gunicorn app:app
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+#app.py
+from flask import Flask,request, url_for, redirect, render_template
+import pickle
+import numpy as np
+
+app = Flask(__name__)
+model=pickle.load(open('model.pkl','rb'))
+
+@app.route('/')
+def home():
+    return render_template("index.html")
+
+@app.route('/predict',methods=['POST','GET'])
+def predict():
+    # receive the values send by user in three text boxes thru request object -> requesst.form.values()
+    
+    int_features = [int(x) for x in request.form.values()]
+    final_features = [np.array(int_features)]
+       
+    prediction=model.predict_proba(final_features)
+    output='{0:.{1}f}'.format(prediction[0][1], 2)
+   
+    return render_template('index.html', pred='Student passing probability is :  {}'.format(output))
+
+if __name__ == '__main__':
+    app.run(debug=False)
@@ -0,0 +1,11 @@
+Flask==1.1.2
+gunicorn==19.9.0
+itsdangerous==1.1.0
+Jinja2==2.11.2
+MarkupSafe==1.1.1
+Werkzeug==1.0.1
+numpy==1.16.6
+scipy==1.2.3
+scikit-learn>=0.18
+matplotlib>=1.4.3
+pandas>=0.19
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+<!DOCTYPE html>
+<html lang="en">
+<head>
+<title> Student Result Prediction </title>
+</head> 
+<body>
+	 
+   <h2>Result Prediction</h2>
+	<h3>Predict the probability of Student Result(Pass/Fail) </h3>
+            
+    <form action='/predict' method="post">
+      <table>
+		<tr><td> Daily Self Study
+			<td>  <input  name="self_study"  placeholder="Daily Self Study (in hrs) " >
+        </tr>
+        <tr><td> Monthly time spent on Tutorials
+            <td>  <input  name="tutorials_montly" placeholder="Time on Tutorials (in hrs)" > 
+		</tr>
+        <tr>
+		<td colspan=2 align=center><br><button type="submit">Predict Probability</button>
+		</tr>
+	  </table>
+    </form>
+       
+     <br> {{pred}}<br>  
+
+  </body>
+</html>
+
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+import pickle  
+import numpy as np
+
+# load the saved model file and use for prediction
+logit_model = pickle.load(open('logit_modelp.pkl','rb'))  
+
+ex2 = np.array([6.2,3.4,5.4,2.3]).reshape(1,-1)
+print ( logit_model.predict(ex2) )