Update app.py

app.py

@@ -6,31 +6,34 @@ import torch
 # Configuración del modelo y tokenizer
 model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
 adapter = "nmarafo/Mistral-7B-Instruct-v0.2-TrueFalse-Feedback-GPTQ"
+
+# Carga el modelo y el tokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, return_token_type_ids=False)
 tokenizer.pad_token = tokenizer.eos_token
-model = AutoPeftModelForCausalLM.from_pretrained(model_id, adapter_name=adapter).cuda()
+model = AutoPeftModelForCausalLM.from_pretrained(adapter, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map="cuda")
 
-def generate_response(question, best_answer, student_answer):
+def generate_prompt(question, best_answer, student_answer):
     system_message = "Analyze the question, the expected answer, and the student's response. Determine if the student's answer is conceptually correct in relation to the expected answer, regardless of the exact wording. Return True if the student's answer is correct or False otherwise. Add a brief comment explaining the rationale behind the answer being correct or incorrect."
     prompt = f"{system_message}\n\nQuestion: {question}\nExpected Answer: {best_answer}\nStudent Answer: {student_answer}"
-    prompt_template = f'<s>[INST] {prompt} [/INST]'
-    input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+    return prompt
+
+def generate_response(prompt):
+    input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
     output = model.generate(input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
     response = tokenizer.decode(output[0], skip_special_tokens=True)
     return response
 
-# Crear la interfaz de usuario en Streamlit
+# Crear la interfaz de usuario con Streamlit
 st.title("Evaluador de Respuestas con GPTQ")
 
-# Creación del formulario
-with st.form("evaluation_form"):
-    question = st.text_input("Pregunta", "")
-    best_answer = st.text_input("Mejor Respuesta", "")
-    student_answer = st.text_input("Respuesta del Estudiante", "")
-    # Botón de envío para el formulario
-    submitted = st.form_submit_button("Evaluar")
+with st.form(key='eval_form'):
+    question = st.text_input("Pregunta")
+    best_answer = st.text_input("Mejor Respuesta")
+    student_answer = st.text_input("Respuesta del Estudiante")
+    submit_button = st.form_submit_button(label='Evaluar')
 
-if submitted:
-    response = generate_response(question, best_answer, student_answer)
+if submit_button:
+    prompt = generate_prompt(question, best_answer, student_answer)
+    response = generate_response(prompt)
     st.write("Respuesta del Modelo:", response)