Update app.py

app.py

@@ -1,40 +1,20 @@
 import streamlit as st
-from ctransformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-# Configuration de la page Streamlit
-st.set_page_config(page_title="Assistant Mathématique", page_icon="🔢", layout="wide")
-
-@st.cache_resource
-def load_model():
-    """Charge le modèle et le tokenizer (mise en cache par Streamlit)"""
-    model_name = "analist/deepseek-math-gguf"  # Remplacez par votre nom de modèle
-    
-    model = AutoModelForCausalLM.from_pretrained(
-    "analist/deepseek-math-gguf", model_file="model.gguf"
-)
-    tokenizer = AutoTokenizer.from_pretrained(model)
-    return model, tokenizer
-
-def generate_response(prompt, model, tokenizer):
-    """Génère une réponse à partir du prompt"""
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=1200,
-            temperature=0.7,
-            do_sample=True,
-            top_p=0.95,
+class MathTutor:
+    def __init__(self):
+        self.model_id = "your-username/deepseek-math-tutor-cpu"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.float32,
+            low_cpu_mem_usage=True,
+            device_map="cpu"
         )
-    
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response.split("### Response:")[-1].strip()
-
-def format_prompt(question):
-    """Formate le prompt comme pendant l'entraînement"""
-    return f"""Below is an instruction that describes a task, paired with an input that provides further context.
+        
+    def get_response(self, question):
+        prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context.
 Write a response that appropriately completes the request.
 Before answering, think carefully about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.
 Your goal is to teach maths a beginner so make it friendly and accessible. Break down your chain of thoughts as for him/her to understand.
@@ -46,56 +26,42 @@ Please answer the following questions.
 ### Question:
 {question}
 
-### Response:"""
+### Response:
+<think>"""
+        
+        inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024)
+        outputs = self.model.generate(
+            **inputs, 
+            max_new_tokens=1200,
+            temperature=0.7,
+            do_sample=True
+        )
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 def main():
-    # Titre de l'application
-    st.title("🔢 Assistant Mathématique")
-    st.markdown("---")
-
-    # Chargement du modèle
-    with st.spinner("Chargement du modèle..."):
-        model, tokenizer = load_model()
-
-    # Initialisation de l'historique des messages dans la session state
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-
-    # Affichage de l'historique des messages
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-    # Zone de saisie utilisateur
-    if question := st.chat_input("Posez votre question mathématique..."):
-        # Afficher la question de l'utilisateur
-        with st.chat_message("user"):
-            st.markdown(question)
-        st.session_state.messages.append({"role": "user", "content": question})
-
-        # Générer et afficher la réponse
-        with st.chat_message("assistant"):
-            with st.spinner("Réflexion en cours..."):
-                prompt = format_prompt(question)
-                response = generate_response(prompt, model, tokenizer)
-                response = response.replace('<think>', '')
-                st.markdown(response)
-        st.session_state.messages.append({"role": "assistant", "content": response})
-
-    # Bouton pour effacer l'historique
-    if st.sidebar.button("Effacer l'historique"):
-        st.session_state.messages = []
-        st.rerun()
-
-    # Informations dans la barre latérale
-    with st.sidebar:
-        st.markdown("### À propos")
-        st.markdown("""
-        Cet assistant utilise un modèle DeepSeek spécialement entraîné pour:
-        - Expliquer les concepts mathématiques
-        - Résoudre des problèmes étape par étape
-        - Fournir des explications claires et adaptées aux débutants
-        """)
+    st.title("🧮 Friendly Math Tutor")
+    st.write("Ask me any math question! I'll help you understand step by step.")
+    
+    tutor = MathTutor()
+    
+    question = st.text_area("Your math question:", height=100)
+    
+    if st.button("Get Help"):
+        if question:
+            with st.spinner("Thinking..."):
+                response = tutor.get_response(question)
+                explanation = response.split("### Response:")[1]
+                st.markdown(explanation)
+        else:
+            st.warning("Please enter a question!")
+            
+    st.divider()
+    st.markdown("""
+    Example questions:
+    - How do I solve quadratic equations?
+    - Explain the concept of derivatives
+    - Help me understand trigonometry ratios
+    """)
 
 if __name__ == "__main__":
     main()