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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel, PeftConfig
import torch
import re
class MoroccanStudentChatbot:
def __init__(self, adapter_model_id="echarif/llama_alpaca_lora_adapter"):
"""Initialize the chatbot with the fine-tuned PEFT/LoRA model"""
print("Loading PEFT adapter config...")
self.config = PeftConfig.from_pretrained(adapter_model_id)
print("Loading base model...")
self.base_model = AutoModelForCausalLM.from_pretrained(
self.config.base_model_name_or_path,
return_dict=True,
device_map="auto",
torch_dtype=torch.float16
)
print("Loading LoRA adapter...")
self.model = PeftModel.from_pretrained(self.base_model, adapter_model_id)
print("Loading tokenizer...")
self.tokenizer = AutoTokenizer.from_pretrained(self.config.base_model_name_or_path)
# Add padding token if it doesn't exist
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
self.alpaca_prompt = """Ci-dessous se trouve une instruction décrivant une tâche, accompagnée éventuellement d'un contexte supplémentaire. Rédige une réponse qui complète correctement la demande.
### Instruction :
{}
### input :
{}
### output :
{}"""
print("Model loaded successfully!")
def clean_output(self, raw_output):
"""Clean the model output to remove unwanted tokens and formatting"""
# Decode the output
if isinstance(raw_output, torch.Tensor):
text = self.tokenizer.decode(raw_output[0], skip_special_tokens=True)
else:
text = raw_output
# Remove the prompt part and keep only the actual response
# Look for the "### output :" pattern and extract what comes after
output_pattern = r"### output :\s*(.*?)(?:<\|end_of_text\|>|$)"
match = re.search(output_pattern, text, re.DOTALL)
if match:
response = match.group(1).strip()
else:
# Fallback: try to extract text after "### output :"
if "### output :" in text:
response = text.split("### output :")[1].strip()
else:
response = text
# Clean up any remaining special tokens
response = re.sub(r'<\|.*?\|>', '', response)
response = re.sub(r'<.*?>', '', response)
# Remove extra whitespace and newlines
response = re.sub(r'\n+', '\n', response)
response = response.strip()
return response if response else "Je suis désolé, je n'ai pas pu générer une réponse appropriée."
def generate_response(self, user_input, history):
"""Generate response for the chatbot"""
if not user_input.strip():
return history, ""
# Format the prompt
prompt = self.alpaca_prompt.format(user_input.strip(), "", "")
# Tokenize
inputs = self.tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
# Generate response
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_p=0.9,
pad_token_id=self.tokenizer.eos_token_id,
repetition_penalty=1.1,
)
# Clean the output
response = self.clean_output(outputs)
# Update history
history.append([user_input, response])
return history, ""
def create_interface():
"""Create the Gradio interface"""
# Initialize the chatbot
chatbot = MoroccanStudentChatbot()
# Custom CSS for professional styling and responsiveness
custom_css = """
.header-container {
display: flex;
justify-content: space-between;
align-items: center;
padding: 20px;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
border-radius: 10px;
margin-bottom: 20px;
color: white;
}
.logo-container {
display: flex;
align-items: center;
gap: 15px;
}
.logo-placeholder {
width: 60px;
height: 60px;
background: rgba(255, 255, 255, 0.2);
border-radius: 10px;
display: flex;
align-items: center;
justify-content: center;
font-size: 24px;
border: 2px solid rgba(255, 255, 255, 0.3);
}
.title-section h1 {
margin: 0;
font-size: 24px;
font-weight: bold;
}
.title-section p {
margin: 5px 0 0 0;
font-size: 14px;
opacity: 0.9;
}
.university-info {
text-align: right;
font-size: 12px;
opacity: 0.8;
}
/* Responsive design */
@media (max-width: 768px) {
.header-container {
flex-direction: column;
text-align: center;
gap: 15px;
}
.university-info {
text-align: center;
}
.title-section h1 {
font-size: 20px;
}
.logo-placeholder {
width: 50px;
height: 50px;
font-size: 20px;
}
}
.chatbot-container {
max-width: 800px;
margin: 0 auto;
}
.footer-info {
text-align: center;
margin-top: 20px;
padding: 15px;
background: #f8f9fa;
border-radius: 8px;
font-size: 12px;
color: #666;
}
/* Custom chatbot styling */
.gradio-container {
max-width: 1200px !important;
}
/* Improve mobile responsiveness */
@media (max-width: 480px) {
.header-container {
padding: 15px;
}
.title-section h1 {
font-size: 18px;
}
.logo-placeholder {
width: 40px;
height: 40px;
font-size: 16px;
}
}
"""
# Create the interface
with gr.Blocks(css=custom_css, title="Assistant Étudiant Marocain", theme=gr.themes.Soft()) as interface:
# Header with logos and title
gr.HTML("""
<div class="header-container">
<div class="logo-container">
<div class="logo-placeholder">🎓</div>
<div class="title-section">
<h1>Assistant Étudiant Marocain</h1>
<p>Votre guide pour l'enseignement supérieur au Maroc</p>
</div>
</div>
<div class="university-info">
<div class="logo-placeholder">🏛️</div>
<div style="margin-top: 5px;">
<strong>Université [Nom de votre université]</strong><br>
Master [Votre spécialité]<br>
Projet de fin d'études
</div>
</div>
</div>
""")
with gr.Row():
with gr.Column(scale=1):
# Main chatbot interface
chatbot_interface = gr.Chatbot(
height=500,
placeholder="👋 Bonjour! Je suis votre assistant pour l'enseignement supérieur au Maroc. Posez-moi vos questions sur les universités, les inscriptions, les bourses, etc.",
container=True,
bubble_full_width=False,
show_label=False,
elem_classes="chatbot-container"
)
with gr.Row():
msg = gr.Textbox(
placeholder="Tapez votre question ici...",
container=False,
scale=7,
min_width=0,
)
send_btn = gr.Button("Envoyer", variant="primary", scale=1, min_width=0)
# Clear button
clear_btn = gr.Button("Nouvelle conversation", variant="secondary", size="sm")
# Example questions
gr.HTML("""
<div style="margin-top: 20px;">
<h3>💡 Questions d'exemple :</h3>
<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 10px; margin-top: 10px;">
<button onclick="document.querySelector('textarea').value='Quelles sont les meilleures universités pour étudier l\\'informatique au Maroc?'; document.querySelector('textarea').focus();"
style="padding: 10px; border: 1px solid #ddd; border-radius: 5px; background: #f8f9fa; cursor: pointer;">
🏫 Universités d'informatique
</button>
<button onclick="document.querySelector('textarea').value='Comment obtenir une bourse d\\'études au Maroc?'; document.querySelector('textarea').focus();"
style="padding: 10px; border: 1px solid #ddd; border-radius: 5px; background: #f8f9fa; cursor: pointer;">
💰 Bourses d'études
</button>
<button onclick="document.querySelector('textarea').value='Quelles sont les procédures d\\'inscription dans les universités publiques?'; document.querySelector('textarea').focus();"
style="padding: 10px; border: 1px solid #ddd; border-radius: 5px; background: #f8f9fa; cursor: pointer;">
📝 Procédures d'inscription
</button>
<button onclick="document.querySelector('textarea').value='Comment trouver un logement étudiant au Maroc?'; document.querySelector('textarea').focus();"
style="padding: 10px; border: 1px solid #ddd; border-radius: 5px; background: #f8f9fa; cursor: pointer;">
🏠 Logement étudiant
</button>
</div>
</div>
""")
# Footer
gr.HTML("""
<div class="footer-info">
<p><strong>Projet de fin d'études</strong> - Développé par [Votre nom]</p>
<p>Ce chatbot utilise un modèle LLaMA 3.1 fine-tuné pour assister les étudiants marocains</p>
<p><em>Pour toute question technique, contactez [[email protected]]</em></p>
</div>
""")
# Event handlers
def respond(message, history):
return chatbot.generate_response(message, history)
def clear_conversation():
return [], ""
# Connect the events
msg.submit(respond, [msg, chatbot_interface], [chatbot_interface, msg])
send_btn.click(respond, [msg, chatbot_interface], [chatbot_interface, msg])
clear_btn.click(clear_conversation, None, [chatbot_interface, msg])
return interface
if __name__ == "__main__":
# Launch the interface
interface = create_interface()
interface.launch(
server_name="0.0.0.0", # Makes it accessible from other devices on the network
server_port=7860,
share=True, # Creates a public link
debug=True,
show_error=True,
inbrowser=True, # Opens automatically in browser
)
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