Merge pull request #9 from luciferreeves/main

Reqs added

3 files changed, 161 insertions, 10 deletions

diff --git a/src/windows/modeltrainer.py b/src/windows/modeltrainer.py
index 25b49c9..1d332fe 100644
--- a/src/windows/modeltrainer.py
+++ b/src/windows/modeltrainer.py
@@ -148,7 +148,7 @@ class ModelTrainer(customtkinter.CTkToplevel):
         # select only text, subreddit, link_flair_text, distinguished, hour, day, ups, num_comments
         self.posts_ups = self.posts[self.categorical_features + self.text_features + ['ups']]
         self.posts_num_comments = self.posts[self.categorical_features + self.text_features + ['num_comments']]
-        self.tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')
+        self.tfidf = TfidfVectorizer()
         self.label_binarizer = LabelBinarizer()
 
         self.edit_textbox('Preparing Data (Upvotes)', 1, 'wait')
diff --git a/src/windows/plotviewer.py b/src/windows/plotviewer.py
index da3886e..40af100 100644
--- a/src/windows/plotviewer.py
+++ b/src/windows/plotviewer.py
@@ -14,6 +14,7 @@ from matplotlib.figure import Figure
 
 from helpers.subreddits import SUBREDDITS
 from .modeltrainer import ModelTrainer
+from .predict import Predict
 import numpy as np
 
 
@@ -49,6 +50,7 @@ class PlotViewer(customtkinter.CTk):
 
     def create_tabs(self):
         self.tabview = customtkinter.CTkTabview(self)
+        self.tabview.add("View Data / Predictions")
         self.tabview.add("Posts")
         self.tabview.add("Subscribers")
         self.tabview.add("Author Activity")
@@ -58,7 +60,6 @@ class PlotViewer(customtkinter.CTk):
         self.tabview.add("Best Time Analysis")
         self.tabview.add("Scores Boxplot")
         self.tabview.add("Scores vs Comments")
-        self.tabview.add("View Data / Predictions")
 
         fig = Figure(figsize=(12, 8), dpi=72)
         self.posts_plot = fig.add_subplot(111)
@@ -242,13 +243,46 @@ class PlotViewer(customtkinter.CTk):
             self.models_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Train Models", command=self.train_models)
             self.models_button.pack()
         else:
-            self.models_label = customtkinter.CTkLabel(self.tabview.tab("View Data / Predictions"), text="Models found. Predict by entering data on the next screen.", pady= 10)
-            self.models_label.pack()
-            self.models_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Predict")
-            self.models_button.pack()
+            self.show_model_options()
 
         self.tabview.pack(expand=True, fill='both')
 
+    def show_model_options(self):
+        models = [
+            'DummyRegressor',
+            'LinearRegression',
+            'RidgeCV',
+            'KNeighborsRegressor',
+            'DecisionTreeRegressor',
+            'RandomForestRegressor',
+            'GradientBoostingRegressor',
+        ]
+        self.model = customtkinter.CTkComboBox(self.tabview.tab("View Data / Predictions"), values=models)
+        self.model.pack(pady=10, padx=10, side=tkinter.LEFT)
+
+        # metrics buttons
+        self.ups_metrics_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Ups Metrics")
+        self.ups_metrics_button.pack(pady=10, padx=10, side=tkinter.LEFT)
+
+        self.num_comments_metrics_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Num Comments Metrics")
+        self.num_comments_metrics_button.pack(pady=10, padx=10, side=tkinter.LEFT)
+
+        # button for model plots
+        self.model_plots_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Model Plots")
+        self.model_plots_button.pack(pady=10, padx=10, side=tkinter.RIGHT)
+
+        # button for predicting
+        self.predict_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Predict a new post", command=self.predict)
+        self.predict_button.pack(pady=10, padx=10, side=tkinter.RIGHT)
+
+
+    def predict(self):
+        # child window to take input of the post - title, selftext, subreddit, day, hour, distinguished
+        pred_win = Predict(self)
+        self.wait_window(pred_win)
+        
+
+
     def train_models(self):
         # open model training child window
         mt = ModelTrainer(self, self.posts)
@@ -258,9 +292,6 @@ class PlotViewer(customtkinter.CTk):
 
         self.models_label.destroy()
         self.models_button.destroy()
-        self.models_label = customtkinter.CTkLabel(self.tabview.tab("View Data / Predictions"), text="Models found. Predict by entering data on the next screen.", pady= 10)
-        self.models_label.pack()
-        self.models_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Predict")
-        self.models_button.pack()
+        self.show_model_options()
 
 
diff --git a/src/windows/predict.py b/src/windows/predict.py
new file mode 100644
index 0000000..374ee09
--- /dev/null
+++ b/src/windows/predict.py
@@ -0,0 +1,120 @@
+import random
+import math
+import customtkinter
+import tkinter
+import pandas as pd
+import os 
+import pickle
+from scipy.sparse import hstack
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import LabelBinarizer
+from .modeltrainer import preprocess
+import tkinter.messagebox
+
+class Predict(customtkinter.CTkToplevel):
+    model_dir = "models/"
+    def __init__(self, parent):
+        super().__init__(parent)
+        self.parent = parent
+        self.title("Predictions")
+        self.grab_set()
+        self.focus_set()
+        posx = int(self.winfo_screenwidth()/2 - 150)
+        posy = int(self.winfo_screenheight()/2 - 350)
+        self.geometry("300x650+{}+{}".format(posx, posy))
+        self.resizable(False, False)
+
+        # title
+        self.title_label = customtkinter.CTkLabel(self, text="Title", anchor='w')
+        self.title_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
+        self.title_entry = customtkinter.CTkEntry(self, width=240)
+        self.title_entry.pack(pady=10, padx=10)
+        self.title_entry.insert(0, 'A good openCV tutorial?')
+
+        # selftext
+        self.selftext_label = customtkinter.CTkLabel(self, text="Selftext", anchor='w')
+        self.selftext_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
+        self.selftext_entry = customtkinter.CTkTextbox(self, width=240, height=100)
+        self.selftext_entry.pack(pady=10, padx=10)
+        self.selftext_entry.insert("0.0", "So I'm learning openCV in python, and now I want as a project to develop some score calculator for a scrabble game. I watched this tutorial from codecamp, and i read about of functionalities of opencv module (such as medianblur, gaussianblur, addweighted, Canny, threshold, and so on), but i still can't grasp it together. Like, i know how to blur an image, to reduce noise let's say, but i don't know when to do that, and especially, why and how much, so I'm searching for a good openCV tutorial that explains these situations. \n As an example, yesterday I did a project where i would've get a sudoku box from an image(by getting the top left, top right, bottom left, bottom right corners of the sudoku box). However, when I tried the same code for the project with the scrabble board, it's a total mess.")
+
+        # subreddit
+        self.subreddit_label = customtkinter.CTkLabel(self, text="Subreddit", anchor='w')
+        self.subreddit_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
+
+        self.subreddit_entry = customtkinter.CTkOptionMenu(self, values=self.parent.posts['subreddit'].unique(), width=240)
+        self.subreddit_entry.pack(pady=10, padx=10)
+        self.subreddit_entry.set(self.parent.posts['subreddit'].unique()[0])
+
+        # day
+        self.day_label = customtkinter.CTkLabel(self, text="Day", anchor='w')
+        self.day_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
+        self.day_entry = customtkinter.CTkOptionMenu(self, values=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], width=240)
+        self.day_entry.pack(pady=10, padx=10)
+        self.day_entry.set('Monday')
+
+        # hour
+        self.hour_label = customtkinter.CTkLabel(self, text="Hour", anchor='w')
+        self.hour_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w')
+
+        hours = ['{}:00'.format(i) for i in range(24)]
+
+        self.hour_entry = customtkinter.CTkOptionMenu(self, values=hours, width=240)
+        self.hour_entry.pack(pady=10, padx=10)
+        self.hour_entry.set('10:00')
+        
+        # distinguished
+        self.distinguished_entry = customtkinter.CTkCheckBox(self, text="Distinguished")
+        self.distinguished_entry.pack(pady=10, padx=10)
+
+        # button for predicting
+        self.predict_button = customtkinter.CTkButton(self, text="Predict", command=self.predict)
+        self.predict_button.pack(pady=10, padx=10)
+
+        # button for closing the window
+        self.close_button = customtkinter.CTkButton(self, text="Close", command=self.destroy)
+        self.close_button.pack(pady=10, padx=10)
+
+    def predict(self):
+        title = self.title_entry.get()
+        selftext = self.selftext_entry.get("1.0", tkinter.END)
+        subreddit = self.subreddit_entry.get()
+        day = self.day_entry.get()
+        hour = self.hour_entry.get()
+        hour = int(hour.split(':')[0])
+        distinguished = self.distinguished_entry.get()
+
+        if not title or not selftext or not subreddit or not day or not hour:
+            tkinter.messagebox.showerror('Error', 'Please fill all the fields')
+            return
+
+        # load the model
+        ups_model = pickle.load(open(os.path.join(self.model_dir, "DummyRegressor_ups.pkl"), 'rb'))
+        num_comments_model = pickle.load(open(os.path.join(self.model_dir, "DummyRegressor_num_comments.pkl"), 'rb'))
+
+        text = title + " " + selftext
+        text = preprocess(text)
+
+        post = pd.DataFrame({
+            'text': [text],
+            'subreddit': [subreddit],
+            'day': [day],
+            'hour': [hour],
+            'distinguished': [distinguished]
+        })
+
+        self.tfidf_vectorizer = TfidfVectorizer()
+        self.label_binarizer = LabelBinarizer()
+
+        post_cat = [self.label_binarizer.fit_transform(post[col]) for col in ['subreddit', 'day', 'hour', 'distinguished']]
+        post_text = self.tfidf_vectorizer.fit_transform(post['text'])
+        postX = hstack([post_text] + post_cat).tocsr()
+
+        ups = int(ups_model.predict(postX)[0])
+        num_comments = int(num_comments_model.predict(postX)[0])
+
+        # random bias from the prediction
+        ups = int(ups // math.log(ups + 1))
+        num_comments = int(num_comments // math.log(num_comments + 1))
+
+        tkinter.messagebox.showinfo('Predictions', 'Predicted ups: {}\nPredicted num_comments: {}'.format(ups, num_comments))