Changed the inference calling in ml_app. Adde a check for the output in predict.html

Rachana Sreedhar · Rachana Sreedhar · commit 4c59eaafbfea · 2020-01-31T18:32:50.000-05:00
diff --git a/ml_app.py b/ml_app.py
@@ -1,7 +1,7 @@
 """Basic app to demonstrate anatomy of flask"""
 
 from flask import Flask, render_template, request
-import sentiment_analysis.api_sentiment_analysis as senti_api
+import sentiment_analysis.sentiment_analysis_code as senti_api
 app = Flask(__name__)
 
 
@@ -13,24 +13,22 @@ def predict():
     user_input = accept_user_input()
     print('User input = ', user_input)
 
-    # Preprocess input - Remember to import the file from sentiment analysis
-    preprocessed_data = senti_api.preprocess_reviews(user_input)
+    if user_input:
+        # Step 3c-2
+        # Send the preprocessed data to the ML model
+        sentiment = senti_api.get_model_prediction(user_input)
 
-    # Step 3c-2
-    # Send the preprocessed data to the ML model
-    sentiment = test.inference(preprocessed_data)
+        # Send the prediction to the web app
+        return render_template('predict.html', user_input=user_input, predicted_out=sentiment)
 
-    # Send the prediction to the web app
-    return render_template('predict.html', predicted_out=sentiment)
+    return render_template('predict.html')
 
 
 def accept_user_input():
     """Accept user input with forms"""
-
+    user_input = ''
     if request.args:
         user_input = request.args['user_review']
-    else:
-        user_input = ''
 
     return user_input
 
diff --git a/sentiment_analysis/sentiment_analysis_code.py b/sentiment_analysis/sentiment_analysis_code.py
@@ -11,7 +11,7 @@
 import re
 from sklearn.feature_extraction.text import CountVectorizer
 import pickle
-
+import os
 
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import accuracy_score
@@ -98,14 +98,18 @@ def get_model_prediction(user_input):
     user_input_processed = preprocess_reviews(user_input)
 
     # Load saved model - vectorizer
-    saved_vectorizer = pickle.load(open('./model/vectorizer.pkl', 'rb'))
+    #saved_vectorizer = pickle.load(open(r'./model/vectorizer.pkl', 'rb'))
+    saved_vectorizer = pickle.load(open(os.path.join('sentiment_analysis', 'model', 'vectorizer.pkl'), 'rb'))
+
 
     # Get feature vector
     user_data_vector = saved_vectorizer.transform([user_input_processed, ''])[:-1]
     # extra string cuz it expects more than one document to transform
 
     # load saved logreg model
-    saved_model = pickle.load(open('./model/log_reg_model.pkl', 'rb'))
+    #saved_model = pickle.load(open(r'./model/log_reg_model.pkl', 'rb'))
+    saved_model = pickle.load(open(os.path.join('sentiment_analysis', 'model', 'log_reg_model.pkl'), 'rb'))
+
 
     # get prediction from saved model
     result = saved_model.predict(user_data_vector)[0]
diff --git a/templates/predict.html b/templates/predict.html
@@ -10,16 +10,16 @@
 <body>
     <form>
         <p>
-            Enter your review for Madagascar
+            Enter your review for the movie
             <br>
         </p>
         <input type="text" name="user_review">
         <input type="submit" value="Submit">
     </form>
 
 {% if predicted_out %}
-  <h1>The prediction is {{ predicted_out }}!</h1>
-{% endif %}
+  <h1>The prediction for {{ user_input }} is {{ predicted_out }}!</h1>
+    {% endif %}
 
 </body>
 </html>
