Main

app.py

@@ -1,9 +1,64 @@
+import pickle
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import sequence
+
 from flask import Flask, render_template, request
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
 app = Flask(__name__)
+
+model = None
+tokenizer = None
+loaded = False  # Flag to ensure loading happens only once
+
+def load_keras_model():
+    global model
+    try:
+        model = load_model('models/uci_sentimentanalysis.h5')
+        print("Model loaded successfully.")
+    except Exception as e:
+        print(f"Error loading model: {e}")
+
+def load_tokenizer():
+    global tokenizer
+    try:
+        with open('models/tokenizer.pickle', 'rb') as handle:
+            tokenizer = pickle.load(handle)
+        print("Tokenizer loaded successfully.")
+    except Exception as e:
+        print(f"Error loading tokenizer: {e}")
+
+# Befor_first_request was not working with the keras version i have isntalled
+@app.before_request
+def before_request():
+    global loaded
+    if not loaded:
+        load_keras_model()
+        load_tokenizer()
+        loaded = True  # Set the flag to True to prevent reloading
+
+def sentiment_analysis(input_text):
+    if tokenizer is None or model is None:
+        return "Model or tokenizer not loaded."
+
+    user_sequences = tokenizer.texts_to_sequences([input_text])
+    user_sequences_matrix = sequence.pad_sequences(user_sequences, maxlen=1225)
+    prediction = model.predict(user_sequences_matrix)
+
+    return round(float(prediction[0][0]), 2)
+
 @app.route("/", methods=["GET", "POST"])
 def index():
-    # TODO: Write the code that calls the sentiment analysis functions here.
-    # hint: use request.method == "POST"
-    return render_template('form.html')
+    sentiment = {}
+    if request.method == "POST":
+        text = request.form.get("user_text")  # Get user input
+        if text:
+            analyzer = SentimentIntensityAnalyzer()
+            sentiment = analyzer.polarity_scores(text)  # VADER analysis
+            sentiment["custom model positive"] = sentiment_analysis(text)  # Custom model analysis
+
+    return render_template('form.html', sentiment=sentiment)
+
 if __name__ == "__main__":
-    app.run()
+    app.run(debug=True)
+