Update app.py

app.py

@@ -1,20 +1,89 @@
+# import gradio as gr
+# from huggingface_hub import InferenceClient
+
+# """
+# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+# """
+# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+
+# def respond(
+#     message,
+#     history: list[tuple[str, str]],
+#     system_message,
+#     max_tokens,
+#     temperature,
+#     top_p,
+# ):
+#     messages = [{"role": "system", "content": system_message}]
+
+#     for val in history:
+#         if val[0]:
+#             messages.append({"role": "user", "content": val[0]})
+#         if val[1]:
+#             messages.append({"role": "assistant", "content": val[1]})
+
+#     messages.append({"role": "user", "content": message})
+
+#     response = ""
+
+#     for message in client.chat_completion(
+#         messages,
+#         max_tokens=max_tokens,
+#         stream=True,
+#         temperature=temperature,
+#         top_p=top_p,
+#     ):
+#         token = message.choices[0].delta.content
+
+#         response += token
+#         yield response
+
+# """
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# """
+# demo = gr.ChatInterface(
+#     respond,
+#     additional_inputs=[
+#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.95,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+# )
+
+
+# if __name__ == "__main__":
+#     demo.launch()
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
+# Placeholder for a user database
+user_db = {}
+
+# Inference client
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+def signup(username, password):
+    if username in user_db:
+        return "Username already exists. Please choose another one."
+    user_db[username] = password
+    return "Signup successful. You can now log in."
+
+def login(username, password):
+    if username not in user_db:
+        return False, "Username does not exist. Please sign up."
+    if user_db[username] != password:
+        return False, "Incorrect password. Please try again."
+    return True, "Login successful. You can now access the chatbot."
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+def respond(message, history, system_message, max_tokens, temperature, top_p):
     messages = [{"role": "system", "content": system_message}]
 
     for val in history:
@@ -39,25 +108,52 @@ def respond(
         response += token
         yield response
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def create_chatbot_interface():
+    chatbot_interface = gr.ChatInterface(
+        respond,
+        additional_inputs=[
+            gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+        ],
+    )
+    return chatbot_interface
+
+def show_signup_or_login():
+    authenticated = gr.State(False)
+
+    def handle_login(username, password):
+        success, message = login(username, password)
+        if success:
+            authenticated.value = True
+
+        return message
+
+    with gr.Blocks() as auth_demo:
+        with gr.Tabs():
+            with gr.TabItem("Sign Up"):
+                signup_username = gr.Textbox(label="Username")
+                signup_password = gr.Textbox(type="password", label="Password")
+                signup_button = gr.Button("Sign Up")
+                signup_output = gr.Textbox(label="Signup Status")
+
+                signup_button.click(fn=signup, inputs=[signup_username, signup_password], outputs=signup_output)
+
+            with gr.TabItem("Log In"):
+                login_username = gr.Textbox(label="Username")
+                login_password = gr.Textbox(type="password", label="Password")
+                login_button = gr.Button("Log In")
+                login_output = gr.Textbox(label="Login Status")
+
+                login_button.click(fn=handle_login, inputs=[login_username, login_password], outputs=login_output)
+
+        launch_button = gr.Button("Launch Chatbot")
+        chatbot_interface = create_chatbot_interface()
+
+        launch_button.click(fn=lambda: chatbot_interface, inputs=None, outputs=[chatbot_interface])
 
+    auth_demo.launch()
 
 if __name__ == "__main__":
-    demo.launch()
+    show_signup_or_login()