Create app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from transformers import AutoTokenizer
+from peft import AutoPeftModelForCausalLM
+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"
+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()
+
+def generate_response(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()
+    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
+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")
+
+if submitted:
+    response = generate_response(question, best_answer, student_answer)
+    st.write("Respuesta del Modelo:", response)
+