diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
index aeee4828d31dfa4c4d8df17901b0aecf753c2d51..552f88ff80c30cc3e7e22953469faadf6c8a7974 100644
--- a/.idea/inspectionProfiles/Project_Default.xml
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -4,7 +4,7 @@
<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredPackages">
<value>
- <list size="11">
+ <list size="14">
<item index="0" class="java.lang.String" itemvalue="google-auth-oauthlib" />
<item index="1" class="java.lang.String" itemvalue="langchain" />
<item index="2" class="java.lang.String" itemvalue="google-auth-httplib2" />
@@ -16,6 +16,9 @@
<item index="8" class="java.lang.String" itemvalue="sentence-transformers" />
<item index="9" class="java.lang.String" itemvalue="chromadb" />
<item index="10" class="java.lang.String" itemvalue="spacy" />
+ <item index="11" class="java.lang.String" itemvalue="unidecode" />
+ <item index="12" class="java.lang.String" itemvalue="sklearn" />
+ <item index="13" class="java.lang.String" itemvalue="sys" />
</list>
</value>
</option>
diff --git a/__pycache__/language_models.cpython-311.pyc b/__pycache__/language_models.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..75c02867db46e28b9eb205285c3f469574629e0d
Binary files /dev/null and b/__pycache__/language_models.cpython-311.pyc differ
diff --git a/language_models.py b/language_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..12e7ec11d38bd4f61c83971b02685d0515609bb4
--- /dev/null
+++ b/language_models.py
@@ -0,0 +1,38 @@
+import torch
+from transformers import RobertaTokenizerFast, EncoderDecoderModel, pipeline
+
+
+def summarize_text(text_input): # Text should always be a string
+ device = 'cuda' if torch.cuda.is_available() else 'cpu'
+ ckpt = 'mrm8488/camembert2camembert_shared-finetuned-french-summarization'
+ tokenizer = RobertaTokenizerFast.from_pretrained(ckpt)
+ model = EncoderDecoderModel.from_pretrained(ckpt).to(device)
+ inputs = tokenizer([text_input], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
+ input_ids = inputs.input_ids.to(device)
+ attention_mask = inputs.attention_mask.to(device)
+ output = model.generate(input_ids, attention_mask=attention_mask)
+ return tokenizer.decode(output[0], skip_special_tokens=True)
+
+
+def qa_pipeline(text_input, user_question):
+ qa_engine = pipeline(
+ "question-answering",
+ model="cmarkea/distilcamembert-base-qa",
+ tokenizer="cmarkea/distilcamembert-base-qa"
+ )
+ result = qa_engine(
+ context=text_input,
+ question=user_question
+ )
+ return result
+
+
+def flant5_summarizer(text_input):
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ pipe = pipeline(
+ "summarization",
+ model="bofenghuang/flan-t5-large-dialogsum-fr",
+ device=device,
+ )
+ summarized_text = pipe(text_input, max_new_tokens=1024, max_length=2048)[0]["summary_text"] # greedy
+ return summarized_text
diff --git a/requirements.txt b/requirements.txt
index 8268cfcedbd320a495b8ac0f095de49009c82515..02e58833b96f796e8d5f2edc700fd59d63c3bee0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,3 +13,6 @@ scikit-learn
nltk
sys
unidecode
+transformers
+torch
+sentencepiece
\ No newline at end of file
diff --git a/visuel.py b/visuel.py
new file mode 100644
index 0000000000000000000000000000000000000000..11adeb6bf03d6af04cb3355a73045a9ae6c3c274
--- /dev/null
+++ b/visuel.py
@@ -0,0 +1,249 @@
+import sys
+
+import numpy as np
+from PyQt5.QtCore import Qt
+from PyQt5.QtGui import QPalette, QColor, QPixmap
+from PyQt5.QtWidgets import QApplication, QWidget, QHBoxLayout, QVBoxLayout, QTextEdit, QLineEdit, QPushButton, \
+ QSizePolicy, QListWidget, QListWidgetItem, QLabel
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+from language_models import flant5_summarizer
+
+
+def read_text_file(file_path):
+ with open(file_path, 'r', encoding='utf-8') as file:
+ content = file.read().split('\n\n')
+ content1 = [item for item in content if item != ""]
+ return content1
+
+
+def extract_keywords_textblob_french(sentence):
+ stop_words = set(stopwords.words('french'))
+ mots_questions = ['qui', 'quoi', 'où', 'quand', 'pourquoi', 'comment', 'quel', 'quelle', 'quels', 'quelles',
+ 'est-ce que', 'y a-t-il', 'peut-on', 'sont-ils', 'sont-elles', 'combien', 'lequel', 'laquelle',
+ 'lesquels', 'lesquelles', 'est-ce', 'n\'est-ce pas', 'savoir', 'pouvez-vous', 'êtes-vous',
+ 'avez-vous', 'dois-je', 'quelqu\'un', 'quelque chose']
+ stop_words = stop_words.union(mots_questions)
+ words = word_tokenize(sentence, language='french')
+ keywords = [word for word in words if word.lower() not in stop_words]
+ return ' '.join(keywords)
+
+
+def create_vectorial_base(text_lines, min_chars=50):
+ filtered_lines = [line for line in text_lines if len(line) >= min_chars]
+
+ if not filtered_lines:
+ print("No lines with at least 50 characters found.")
+ return None, None, None
+
+ vectorizer = TfidfVectorizer()
+ vectorial_base = vectorizer.fit_transform(filtered_lines).toarray()
+ feature_names = vectorizer.get_feature_names_out()
+
+ return vectorizer, vectorial_base, feature_names
+
+
+def get_best_answers(question, text_lines, vectorizer, vectorial_base):
+ question_vector = vectorizer.transform([question]).toarray()
+
+ # Calculate cosine similarity between the question and each text line
+ similarities = cosine_similarity(question_vector, vectorial_base).flatten()
+
+ # Get the indices of the top 5 most similar text lines
+ top_indices = np.argsort(similarities)[-3:][::-1]
+ # Retrieve the corresponding text lines
+ best_answers = [text_lines[i] + "\n" for i in top_indices]
+
+ return best_answers
+
+
+class WrappingLabel(QLabel):
+ def __init__(self, text='', parent=None):
+ super(WrappingLabel, self).__init__(text, parent)
+ self.setWordWrap(True)
+
+
+class StyledListWidgetItem(QListWidgetItem):
+ def __init__(self, text='', parent=None):
+ super(StyledListWidgetItem, self).__init__(parent)
+ self.setText(text)
+
+ def initStyle(self):
+ palette = QPalette()
+ palette.setColor(QPalette.Highlight,
+ QColor("#4b5261")) # Couleur de fond pour l'élément sélectionné dans la liste d'historique
+ palette.setColor(QPalette.HighlightedText,
+ QColor("#ff0000")) # Couleur du texte pour l'élément sélectionné dans la liste d'historique
+ self.setData(Qt.UserRole, palette)
+
+
+class StyledListWidget(QListWidget):
+ def __init__(self, parent=None):
+ super(StyledListWidget, self).__init__(parent)
+ self.setAlternatingRowColors(False)
+ self.setStyleSheet("""
+ QListWidget {
+ background-color: #282c34; /* Couleur de fond pour la liste d'historique */
+ color: #abb2bf; /* Couleur du texte dans la liste d'historique */
+ border-radius: 10px; /* Coins arrondis */
+ }
+ """)
+
+ def addStyledItem(self, text):
+ item = StyledListWidgetItem(text)
+ item.initStyle()
+ self.addItem(item)
+
+
+class ChatbotInterface(QWidget):
+ def __init__(self):
+ super().__init__()
+ file_path = 'reglementdescolarite-ingegeneraliste2324-1.docx.txt'
+ self.text_lines = read_text_file(file_path)
+
+ if not self.text_lines:
+ print("The file is empty or doesn't exist.")
+ return
+ self.vectorizer, self.vectorial_base, _ = create_vectorial_base(self.text_lines)
+ self.init_ui()
+ self.command_history = [] # Pour stocker l'historique des commandes
+ self.dico = {}
+
+ def init_ui(self):
+ # Créer des widgets
+ self.conversation_text = QTextEdit(self)
+ self.conversation_text.setReadOnly(True)
+
+ self.user_input_entry = QLineEdit(self)
+ self.user_input_entry.setPlaceholderText("Saisissez votre message...")
+ self.user_input_entry.setMinimumHeight(40)
+
+ self.send_button = QPushButton("Envoyer", self)
+ self.send_button.setMinimumSize(self.user_input_entry.width(), 30) # Ajustez selon vos besoins
+ self.send_button.setMaximumSize(200, 60)
+ self.send_button.clicked.connect(self.send_message)
+
+ # Historique à droite
+ self.history_list_widget = StyledListWidget(self)
+ self.history_list_widget.itemClicked.connect(self.history_item_clicked)
+ self.history_list_widget.setFixedWidth(200) # Ajuster la largeur selon vos besoins
+
+ # Configurer la mise en page
+ layout = QVBoxLayout(self)
+ h_layout = QHBoxLayout()
+
+ # Widgets à gauche
+ left_layout = QVBoxLayout()
+ left_layout.addWidget(self.conversation_text)
+ left_layout.addWidget(self.user_input_entry)
+ # Ajouter le bouton "Envoyer" avec une taille réduite
+ self.send_button.setMaximumWidth(self.send_button.width() // 3)
+ left_layout.addWidget(self.send_button, alignment=Qt.AlignRight)
+ h_layout.addLayout(left_layout)
+
+ # Historique à droite
+ h_layout.addWidget(self.history_list_widget)
+ layout.addLayout(h_layout)
+
+ # Configurer la politique de taille pour permettre à la zone de conversation de s'étendre verticalement
+ size_policy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Expanding)
+ self.conversation_text.setSizePolicy(size_policy)
+
+ # Définir la fenêtre principale
+ self.setWindowTitle('Chatbot Interface')
+ self.setGeometry(100, 100, 800, 600)
+
+ # Appliquer les styles
+ self.setStyleSheet("""
+ QWidget {
+ background-color: #282c34; /* Couleur principale de fond pour l'application */
+ color: #abb2bf; /* Couleur du texte principal */
+ }
+
+ QTextEdit, QLineEdit {
+ background-color: #2c313a; /* Couleur de fond pour la zone de texte et d'entrée utilisateur */
+ color: #abb2bf; /* Couleur du texte dans la zone de texte et d'entrée utilisateur */
+ border-radius: 10px; /* Coins arrondis */
+ }
+
+ QPushButton {
+ background-color: #61afef; /* Couleur de fond pour le bouton Envoyer */
+ color: #282c34; /* Couleur du texte sur le bouton Envoyer */
+ border-radius: 10px; /* Coins arrondis */
+ }
+
+
+ """)
+ self.user_input_entry.returnPressed.connect(self.send_message)
+ self.history_list_widget.itemClicked.connect(self.history_item_clicked)
+
+ def send_message(self):
+
+ user_command = self.user_input_entry.text()
+ if len(user_command) > 0:
+ self.conversation_text.clear()
+ self.conversation_text.append(f"Commande utilisateur: {user_command}")
+
+ best_answers = get_best_answers(user_command, self.text_lines, self.vectorizer, self.vectorial_base)
+
+ flant5_summarized_text = "La réponse formulée par flan-T5 (1024) : \n" + flant5_summarizer(
+ " ".join(best_answers))
+
+ chatbot_response = flant5_summarized_text + \
+ "\n\n Les documents utilisés pour formuler cette réponse sont : \n\n\n"
+ for i, answer in enumerate(best_answers, start=1):
+ chatbot_response += (f"{i}. {answer.strip()}\n\n")
+ self.conversation_text.append(chatbot_response)
+
+ # Ajouter la commande à l'historique
+ user_command1 = extract_keywords_textblob_french(user_command)
+ self.command_history.append(user_command1)
+ self.dico[user_command1] = chatbot_response
+
+ # Mettre à jour la liste d'historique
+ self.update_history_list()
+
+ self.user_input_entry.clear()
+ else:
+ pass
+
+ def set_logo(self, image_path):
+ pixmap = QPixmap(image_path)
+ self.logo_label.setPixmap(pixmap)
+ self.logo_label.setScaledContents(True)
+
+ def update_history_list(self):
+ self.history_list_widget.clear()
+ for command in self.command_history:
+ self.history_list_widget.addStyledItem(command)
+
+ def history_item_clicked(self, item):
+ self.conversation_text.clear()
+ # Réafficher le contenu dans la conversation_text lorsque l'élément de l'historique est cliqué
+ selected_index = self.history_list_widget.row(item)
+ if selected_index < len(self.command_history):
+ selected_command = self.command_history[selected_index]
+ self.conversation_text.append(f"Commande utilisateur: {selected_command}")
+ # Traiter la commande et obtenir la réponse du chatbot (vous devrez ajuster cela en fonction de votre application)
+ chatbot_response = f"Réponse du chatbot pour la commande: {self.dico[selected_command]}"
+ self.conversation_text.append(chatbot_response)
+
+
+if __name__ == '__main__':
+ app = QApplication(sys.argv)
+ chatbot_app = ChatbotInterface()
+
+ # Centrer la fenêtre
+ screen = app.primaryScreen()
+ center_point = screen.availableGeometry().center()
+ chatbot_app.move(center_point - chatbot_app.rect().center())
+
+ # Ajuster la taille de la fenêtre
+ new_width = screen.availableGeometry().width() // 2
+ chatbot_app.resize(new_width, screen.availableGeometry().height() * round(7 / 8))
+
+ chatbot_app.show()
+ sys.exit(app.exec_())