Upload 9 files

AI_user.py

@@ -0,0 +1,28 @@
+#load model
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import pickle
+
+model = load_model("model.h5")
+
+#load tokenizer
+with open("tokenizer.pkl","rb") as handle:
+    tokenizer = pickle.load(handle)
+
+#make predictions
+# Make predictions
+while True:
+    text = input("write a review, press e to exit: ")
+    if text == 'e':
+        break
+    TokenText = tokenizer.texts_to_sequences([text])
+    PadText = pad_sequences(TokenText, maxlen=100)
+    Pred = model.predict(PadText)
+    Pred_float = Pred[0][0]  # Extract the single float value
+    Pred_float *= 1.3
+    binary_pred = (Pred_float > 0.5).astype(int)
+    if binary_pred == 0:
+        print("bad review")
+    else:
+        print("good review")
+    print(Pred_float)

Ai.py

@@ -0,0 +1,98 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import nltk
+from nltk.tokenize import word_tokenize
+import pickle
+import tensorflow as tf
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+from sklearn.model_selection import train_test_split
+nltk.download('punkt_tab')
+# load dataset
+DatasetLocation = r"datset.csv"
+dataset = pd.read_csv(DatasetLocation)
+print("data loaded")
+
+#label data
+x = dataset["text"]
+y = dataset["output"]
+
+#convert y 
+#convert -1 to 0
+Newy = y + 1 
+Newy = Newy / 2
+#remove NAN to 0
+#convert 1 to 0.5
+
+y = Newy
+for i in range(len(y)):
+    if np.isnan(y[i]):
+        y[i] = 0
+print(y)
+#tokenize data
+tokenizer = Tokenizer()
+
+#fit tokenizer
+tokenizer.fit_on_texts(x)
+
+TokenX = tokenizer.texts_to_sequences(x)
+
+#save tokenizer
+with open("tokenizer.pkl","wb") as handle:
+    pickle.dump(tokenizer,handle,protocol=pickle.HIGHEST_PROTOCOL)
+
+print(TokenX)
+
+#pad data
+
+max_length = 100  # Choose a suitable maximum length
+X_Padded = pad_sequences(TokenX,maxlen= max_length)
+
+print("data padded correctly")
+
+#set train and validation
+X_train, X_val, y_train, y_val = train_test_split(X_Padded, y, test_size=0.2, random_state=42)
+
+
+# Define the model
+model = Sequential([
+    Dense(256, activation='relu'),
+    Dense(128, activation='relu'),
+    Dense(1, activation='sigmoid')  # For binary classification
+])
+from tensorflow.keras.optimizers import Adam
+
+model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
+print("model defined correctly")
+print(np.isnan(y).sum())  # Should be 0
+# train model
+epochs = 3
+i = 0
+TrainLoss= []
+ValLoss= []
+Num = []
+while i < epochs:
+    history = model.fit(X_Padded, y, epochs=100, verbose=2)
+    Train_loss = history.history['loss'][-1]  # Get the last value of training loss
+    Train_accuracy = history.history['accuracy'][-1]  # Get the last value of training accuracy
+    Val_loss, Val_accuracy = model.evaluate(X_val, y_val)
+    ValLoss.append(Val_loss)
+    TrainLoss.append(Train_loss)
+    Num.append(i)
+    i += 1
+#save the model
+model.save("model.h5")
+#graph loss
+plt.figure(figsize=(10, 6))
+plt.plot(Num, ValLoss, label='Validation Loss', color='orange')
+plt.plot(Num, TrainLoss, label='Training Loss', color='blue')
+plt.title('Training and Validation Loss')
+plt.xlabel('Epochs')
+plt.ylabel('Loss')
+plt.legend()
+plt.grid()
+plt.show()

datset.csv

@@ -0,0 +1,121 @@
+text,output
+
+"This movie was a shit",-1
+
+"The plot was terrible and boring",-1
+
+"I regret watching this movie",-1
+
+"I hate this movie",-1
+
+"This was the worst film I've ever seen.",-1
+
+"I felt like I wasted my time on this movie.",-1
+
+"The storyline was nonsensical and confusing.",-1
+
+"The special effects were awful.",-1
+
+"I couldn't connect with any of the characters.",-1
+
+"The pacing was so slow that I almost fell asleep.",-1
+
+"A complete disappointment from start to finish.",-1
+
+"The dialogue was cringeworthy and unrealistic.",-1
+
+"I can't believe I spent money on this film.",-1
+
+"The ending was predictable and unsatisfying.",-1
+
+"It felt like a chore to sit through this movie.",-1
+
+"I loved this movie",1
+
+"Absolutely brilliant! A must-see for everyone.",1
+
+"The acting was superb, truly a masterpiece!",1
+
+"A delightful experience from start to finish!",1
+
+"An emotional rollercoaster that I loved!",1
+
+"A visually stunning film that captivated me!",1
+
+"This movie made me feel so happy!",1
+
+"A heartwarming story that touched my soul.",1
+
+"The soundtrack was amazing and perfectly matched!",1
+
+"A great blend of humor and drama!",1
+
+"I can't wait to watch it again!",1
+
+"This film inspired me in so many ways!",1
+
+"The cinematography was breathtaking!",1
+
+"Every character had depth and complexity.",1
+
+"It kept me on the edge of my seat the entire time!",
+
+"The film lacked originality and creativity",-1
+
+"The plot twists were predictable and uninspired",-1
+
+"The character development was shallow and rushed",-1
+
+"The direction was lackluster and uninspired",-1
+
+"It felt like a cash grab with no real substance",-1
+
+"The pacing was uneven and poorly executed",-1
+
+"It tried too hard to be funny but failed miserably",-1
+
+"The themes were clichéd and overused",-1
+
+"A missed opportunity for something great",-1
+
+"It felt disjointed and poorly constructed overall",-1
+
+"This movie was just okay nothing special",0
+
+"I had a decent time watching it but wouldn't recommend it",0
+
+"It was fun but not something I would watch again",0
+
+"Some parts were entertaining while others dragged on",0
+
+"I liked it but it didn't blow me away",0
+
+"Not bad for a weekend watch but forgettable",0
+
+"It had its moments but overall it was average",0
+
+"I enjoyed some scenes but the movie felt long",0
+
+"It was fine for what it was nothing more nothing less",0
+
+"I laughed a bit but didn't feel invested in the story",0
+
+"As a horror film it failed to scare me at all",-1
+
+"An action-packed adventure that kept me on my toes",1
+
+"This romantic comedy made me cringe more than laugh",-1
+
+"A thrilling mystery that kept me guessing until the end",1
+
+"The sci-fi elements were fascinating and well done",1
+
+"This drama felt overly melodramatic and forced",-1
+
+"An inspiring sports film that motivated me to chase my dreams",1
+
+"The fantasy world was rich and beautifully crafted",1
+
+"This documentary opened my eyes to important issues in society",1
+
+"A forgettable family film that didn’t resonate with anyone in my house",-1

model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7133f699e53939da1c9f7918efa4045f2ea379773ee68418437c2f0219311b7a
+size 737848

tokenizer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb33b05c184d69db72218673c8af1b25bda6710420dfbb2a2c6077475505f789
+size 6787