no token needed

app.py

@@ -13,10 +13,9 @@ 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")
                 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])
+                load_configs.click(document_chatbot.load_token_and_model, inputs=picked_model)
                 
             chatbot = gr.Chatbot()
 

document_chatbot.py

@@ -25,18 +25,11 @@ class DocumentChatbot:
         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_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:
-            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_token_and_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 "Model and Token successfully loaded"
         
 
     def respond(self, text_input, question, chat_history):