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app.py

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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
+    )

requirements.txt

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+torch
+transformers
+bitsandbytes
+peft
+gradio