reduced the methods in class and add a load config buttion

app.py

@@ -13,10 +13,11 @@ with gr.Blocks() as demo:
         text_input = gr.Textbox(label="Enter text or URL to text file")
         with gr.Column():
             with gr.Row():
-                api_key_input = gr.Textbox(label="Enter HF Token to load the model")
-                api_key_input.submit(document_chatbot.load_token, inputs=api_key_input, outputs=api_key_input)
-                picked_model = gr.Dropdown(["google/flan-t5-large", "google/flan-t5-base","google/flan-t5-small"], label="Models")
-                picked_model.change(document_chatbot.load_model, picked_model)
+                api_key_input = gr.Textbox(label="Enter HF Token")
+                picked_model = gr.Dropdown(["google/flan-t5-large", "google/flan-t5-base","google/flan-t5-small"], label="Models", interactive=True)
+                load_configs = gr.Button("Load configs")
+                load_configs.click(document_chatbot.load_token_and_model, inputs=[api_key_input, picked_model], outputs=[api_key_input])
+                
             chatbot = gr.Chatbot()
 
             q_input = gr.Textbox(label="Please write your question")

document_chatbot.py

@@ -12,6 +12,9 @@ from langchain.vectorstores import FAISS
 from langchain import HuggingFaceHub
 
 
+from os import environ
+
+    
 class DocumentChatbot:
     
     def __init__(self):
@@ -21,23 +24,24 @@ class DocumentChatbot:
         self.metadata = {"source": "internet"}
         self.init_mes = ["According to the document, ", "Based on the text, ", "I think, ", "According to the text, ", "Based on the document you provided, "]
         
-    def load_token(self, api_key):
+    
+    def load_token_and_model(self, api_key, model_name):
+        result = None
         if api_key[:2] == "hf":
             os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
             result = subprocess.run(["curl", "https://huggingface.co/api/whoami-v2", "-H", f"Authorization: Bearer {api_key}"], capture_output=True).stdout.decode()
         if result == '{"error":"Invalid username or password."}':
             return  "Invalid API token"
         else:
-            return "HF Token successfully registered"
+            self.llm = HuggingFaceHub(repo_id=model_name, model_kwargs={"temperature":0, "max_length":512})
+            self.chain = load_qa_chain(self.llm, chain_type="stuff")
+            self.embeddings = HuggingFaceEmbeddings()
+            return "Model and Token successfully loaded"
         
-    def load_model(self, model_name):
-        self.llm = HuggingFaceHub(repo_id=model_name, model_kwargs={"temperature":0, "max_length":512})
-        self.chain = load_qa_chain(self.llm, chain_type="stuff")
-        self.embeddings = HuggingFaceEmbeddings()
-        return f"Successfully loaded {model_name}"
-
 
     def respond(self, text_input, question, chat_history):
+        if not question or question.isspace():
+            return "Please enter a valid question.", chat_history
         if text_input.startswith("http"):
             response = requests.get(text_input)
             text_var = response.text
@@ -72,4 +76,4 @@ class DocumentChatbot:
 
         time.sleep(1)
         
-        return "", chat_history
+        return "", chat_history