init

app.py

@@ -2,7 +2,17 @@ import gradio as gr
 import requests
 
 API_KEY = "Prep@123"
-LCG_SERVICE_URL = "http://bore.testsprep.online:8082/v1/theory_lcg"
+LCG_SERVICE_URL_v1 = "http://bore.testsprep.online:8082/v1/theory_lcg"
+LCG_SERVICE_URL_v2 = "http://bore.testsprep.online:8081/v1/theory_lcg"
+LCG_SERVICE_URL_v3 = "http://bore.testsprep.online:8083/v1/theory_lcg"
+
+
+MODEL2SERVICE = {
+    'llama-3.1-sft-awq': LCG_SERVICE_URL_v1,
+    'hermes-3-llama3.1-sft-lora': LCG_SERVICE_URL_v2,
+    'qwen2-1.5b-full-sft': LCG_SERVICE_URL_v3
+}
+
 
 weights_mapping = {
   'beginner': {
@@ -33,7 +43,7 @@ def get_response_message(config):
         'Content-Type': 'application/json'
     }
     data = {
-        "model": "lcg_sft",
+        "model": config["model_name"],
         "input_data": {
             "user_level": config["user_level"],
             "num_questions": config["num_questions"],
@@ -57,16 +67,19 @@ def get_response_message(config):
         "stop": "string",
         "stream": False
     }
+    try:
+        response = requests.post(MODEL2SERVICE[config["model_name"]], headers=headers, json=data)
+        return response.json()["data"]
+    
+    except:
+        return {"message": f"Hiện tại chúng tôi chưa hỗ trợ mô hình {config["model_name"]}"}
 
-    response = requests.post(LCG_SERVICE_URL, headers=headers, json=data)
-    return response.json()["data"]
-
-
-def generate_questions(user_level, num_questions, question_type, language, explanation_language, context, learning_outcomes, mode):
+def generate_questions(model_name, user_level, num_questions, question_type, language, explanation_language, context, learning_outcomes, mode):
     if mode == "Reviewing" and not context.strip():
         return {"error": "Với chế độ Reviewing, Context không được để trống."}
 
     config = {
+        "model_name": model_name,
         "user_level": user_level,
         "num_questions": int(num_questions),
         "question_type": question_type,
@@ -82,6 +95,7 @@ def generate_questions(user_level, num_questions, question_type, language, expla
 iface = gr.Interface(
     fn=generate_questions,
     inputs=[
+        gr.Dropdown(list(MODEL2SERVICE.keys()), label="Model Usage"),
         gr.Dropdown(["beginner", "intermediate", "advanced"], label="User Level"),
         gr.Number(value=5, label="Number of Questions"),
         gr.Dropdown(["Short answer", "Single choice", "Multiple choice"], label="Question Type"),