Update with h2oGPT hash 8fc21162cdbe751ad32abb13f4e15e090d7af7ce

app.py

@@ -83,6 +83,7 @@ def main(
         # set to True to load --base_model after client logs in,
         # to be able to free GPU memory when model is swapped
         login_mode_if_model0: bool = False,
+        block_gradio_exit: bool = True,
 
         sanitize_user_prompt: bool = True,
         sanitize_bot_response: bool = True,
@@ -116,6 +117,9 @@ def main(
         # must override share if in spaces
         share = False
     save_dir = os.getenv('SAVE_DIR', save_dir)
+    score_model = os.getenv('SCORE_MODEL', score_model)
+    if score_model == 'None':
+        score_model = ''
 
     # get defaults
     model_lower = base_model.lower()
@@ -726,12 +730,12 @@ body.dark{background:linear-gradient(#0d0d0d,#333333);}"""
                                                    placeholder=kwargs['placeholder_input'])
                         submit_nochat = gr.Button("Submit")
                         flag_btn_nochat = gr.Button("Flag")
-                        if kwargs['score_model']:
-                            if not kwargs['auto_score']:
-                                with gr.Column():
-                                    score_btn_nochat = gr.Button("Score last prompt & response")
-                                    score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)
-                            else:
+                        if not kwargs['auto_score']:
+                            with gr.Column(visible=kwargs['score_model']):
+                                score_btn_nochat = gr.Button("Score last prompt & response")
+                                score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)
+                        else:
+                            with gr.Column(visible=kwargs['score_model']):
                                 score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)
                     col_chat = gr.Column(visible=kwargs['chat'])
                     with col_chat:
@@ -751,19 +755,19 @@ body.dark{background:linear-gradient(#0d0d0d,#333333);}"""
                         with gr.Row():
                             clear = gr.Button("New Conversation")
                             flag_btn = gr.Button("Flag")
-                            if kwargs['score_model']:
-                                if not kwargs['auto_score']:  # FIXME: For checkbox model2
-                                    with gr.Column():
-                                        with gr.Row():
-                                            score_btn = gr.Button("Score last prompt & response").style(
-                                                full_width=False, size='sm')
-                                            score_text = gr.Textbox("Response Score: NA", show_label=False)
-                                        score_res2 = gr.Row(visible=False)
-                                        with score_res2:
-                                            score_btn2 = gr.Button("Score last prompt & response 2").style(
-                                                full_width=False, size='sm')
-                                            score_text2 = gr.Textbox("Response Score2: NA", show_label=False)
-                                else:
+                            if not kwargs['auto_score']:  # FIXME: For checkbox model2
+                                with gr.Column(visible=kwargs['score_model']):
+                                    with gr.Row():
+                                        score_btn = gr.Button("Score last prompt & response").style(
+                                            full_width=False, size='sm')
+                                        score_text = gr.Textbox("Response Score: NA", show_label=False)
+                                    score_res2 = gr.Row(visible=False)
+                                    with score_res2:
+                                        score_btn2 = gr.Button("Score last prompt & response 2").style(
+                                            full_width=False, size='sm')
+                                        score_text2 = gr.Textbox("Response Score2: NA", show_label=False)
+                            else:
+                                with gr.Column(visible=kwargs['score_model']):
                                     score_text = gr.Textbox("Response Score: NA", show_label=False)
                                     score_text2 = gr.Textbox("Response Score2: NA", show_label=False, visible=False)
                             retry = gr.Button("Regenerate")
@@ -942,7 +946,6 @@ body.dark{background:linear-gradient(#0d0d0d,#333333);}"""
         fun = partial(evaluate,
                       **kwargs_evaluate)
         fun2 = partial(evaluate,
-                       model_state2,
                        **kwargs_evaluate)
 
         dark_mode_btn = gr.Button("Dark Mode", variant="primary").style(
@@ -1042,25 +1045,31 @@ body.dark{background:linear-gradient(#0d0d0d,#333333);}"""
             os.environ['TOKENIZERS_PARALLELISM'] = 'true'
             return 'Response Score: {:.1%}'.format(score)
 
+        def noop_score_last_response(*args, **kwargs):
+            return "Response Score: Disabled"
         if kwargs['score_model']:
-            score_args = dict(fn=score_last_response,
-                              inputs=inputs_list + [text_output],
-                              outputs=[score_text],
-                              )
-            score_args2 = dict(fn=partial(score_last_response, model2=True),
-                               inputs=inputs_list + [text_output2],
-                               outputs=[score_text2],
-                               )
+            score_fun = score_last_response
+        else:
+            score_fun = noop_score_last_response
+
+        score_args = dict(fn=score_fun,
+                          inputs=inputs_list + [text_output],
+                          outputs=[score_text],
+                          )
+        score_args2 = dict(fn=partial(score_fun, model2=True),
+                           inputs=inputs_list + [text_output2],
+                           outputs=[score_text2],
+                           )
 
-            score_args_nochat = dict(fn=partial(score_last_response, nochat=True),
-                                     inputs=inputs_list + [text_output_nochat],
-                                     outputs=[score_text_nochat],
-                                     )
-            if not kwargs['auto_score']:
-                score_event = score_btn.click(**score_args, queue=stream_output, api_name='score') \
-                    .then(**score_args2, queue=stream_output, api_name='score2')
-                score_event_nochat = score_btn_nochat.click(**score_args_nochat, queue=stream_output,
-                                                            api_name='score_nochat')
+        score_args_nochat = dict(fn=partial(score_fun, nochat=True),
+                                 inputs=inputs_list + [text_output_nochat],
+                                 outputs=[score_text_nochat],
+                                 )
+        if not kwargs['auto_score']:
+            score_event = score_btn.click(**score_args, queue=stream_output, api_name='score') \
+                .then(**score_args2, queue=stream_output, api_name='score2')
+            score_event_nochat = score_btn_nochat.click(**score_args_nochat, queue=stream_output,
+                                                        api_name='score_nochat')
 
         def user(*args, undo=False, sanitize_user_prompt=True, model2=False):
             """
@@ -1416,14 +1425,15 @@ body.dark{background:linear-gradient(#0d0d0d,#333333);}"""
         stop_btn.click(lambda: None, None, None,
                        cancels=[submit_event_nochat, submit_event, submit_event2, submit_event3],
                        queue=False, api_name='stop').then(clear_torch_cache)
-        demo.load(None,None,None,_js=dark_js)
+        demo.load(None,None,None, _js=dark_js)
 
     demo.queue(concurrency_count=1)
     favicon_path = "h2o-logo.svg"
     demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True,
                 favicon_path=favicon_path, prevent_thread_lock=True)  # , enable_queue=True)
     print("Started GUI", flush=True)
-    demo.block_thread()
+    if kwargs['block_gradio_exit']:
+        demo.block_thread()
 
 
 input_args_list = ['model_state']

client_test.py

@@ -13,43 +13,69 @@ Currently, this will force model to be on a single GPU.
 Then run this client as:
 
 python client_test.py
+
+
+
+For HF spaces:
+
+HOST="https://h2oai-h2ogpt-chatbot.hf.space" python client_test.py
+
+Result:
+
+Loaded as API: https://h2oai-h2ogpt-chatbot.hf.space ✔
+{'instruction_nochat': 'Who are you?', 'iinput_nochat': '', 'response': 'I am h2oGPT, a large language model developed by LAION.'}
+
+
+For demo:
+
+HOST="https://gpt.h2o.ai" python client_test.py
+
+Result:
+
+Loaded as API: https://gpt.h2o.ai ✔
+{'instruction_nochat': 'Who are you?', 'iinput_nochat': '', 'response': 'I am h2oGPT, a chatbot created by LAION.'}
+
 """
 
 debug = False
 
 import os
 os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
-from gradio_client import Client
-
-client = Client("http://localhost:7860")
-if debug:
-    print(client.view_api(all_endpoints=True))
-
-instruction = ''  # only for chat=True
-iinput = ''  # only for chat=True
-context = ''
-# streaming output is supported, loops over and outputs each generation in streaming mode
-# but leave stream_output=False for simple input/output mode
-stream_output = False
-prompt_type = 'human_bot'
-temperature = 0.1
-top_p = 0.75
-top_k = 40
-num_beams = 1
-max_new_tokens = 50
-min_new_tokens = 0
-early_stopping = False
-max_time = 20
-repetition_penalty = 1.0
-num_return_sequences = 1
-do_sample = True
-# only these 2 below used if pass chat=False
-chat = False
-instruction_nochat = "Who are you?"
-iinput_nochat = ''
+
+
+def get_client():
+    from gradio_client import Client
+
+    client = Client(os.getenv('HOST', "http://localhost:7860"))
+    if debug:
+        print(client.view_api(all_endpoints=True))
+    return client
 
 
 def test_client_basic():
+    instruction = ''  # only for chat=True
+    iinput = ''  # only for chat=True
+    context = ''
+    # streaming output is supported, loops over and outputs each generation in streaming mode
+    # but leave stream_output=False for simple input/output mode
+    stream_output = False
+    prompt_type = 'human_bot'
+    temperature = 0.1
+    top_p = 0.75
+    top_k = 40
+    num_beams = 1
+    max_new_tokens = 50
+    min_new_tokens = 0
+    early_stopping = False
+    max_time = 20
+    repetition_penalty = 1.0
+    num_return_sequences = 1
+    do_sample = True
+    # only these 2 below used if pass chat=False
+    chat = False
+    instruction_nochat = "Who are you?"
+    iinput_nochat = ''
+
     args = [instruction,
             iinput,
             context,
@@ -71,12 +97,14 @@ def test_client_basic():
             iinput_nochat,
             ]
     api_name = '/submit_nochat'
+    client = get_client()
     res = client.predict(
         *tuple(args),
         api_name=api_name,
     )
     res_dict = dict(instruction_nochat=instruction_nochat, iinput_nochat=iinput_nochat, response=md_to_text(res))
     print(res_dict)
+    return res_dict
 
 
 import markdown  # pip install markdown