version 2 with a real Chat service

LICENSE.txt

@@ -0,0 +1,438 @@
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README.md

@@ -1,10 +1,10 @@
 ---
 title: Claire Chat
-emoji: 🎙💬
+emoji: 🗣️🇫🇷💬
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 4.4.0
+sdk_version: 4.4.1
 app_file: app.py
 pinned: true
 license: cc-by-nc-sa-4.0

app.py

@@ -1,18 +1,149 @@
 import gradio as gr
+from threading import Thread
 import transformers
-from transformers import AutoConfig, AutoTokenizer, AutoModel, AutoModelForCausalLM
+import spaces
 import torch
 import unicodedata
-import re
+import regex as re
+
+# Model
+model_name = "OpenLLM-France/Claire-7B-0.1"
+
+# Title and description
+title = "Conversation avec Claire"
+description = """\
+Simulation de conversation en Français avec [OpenLLM-France/Claire-7B](https://huggingface.co/OpenLLM-France/Claire-7B-0.1).
+<strong>Claire n'est <u>pas</u> un assistant personnel</strong>, elle a tendance à comprendre et répondre un <b>langage parlé</b>, \
+peut faire preuve d'humour, et <strong>ne vous dira <u>pas</u> (forcément) des vérités</strong>.
+"""
 
 # Default variables
-default_max_new_tokens = 100
+default_max_new_tokens = 200
 default_temperature = 1.0
+default_repetition_penalty = 1.5
 default_top_k = 10
 default_top_p = 0.99
-default_repetition_penalty = 1.0
 
-model_name = "OpenLLM-France/Claire-7B-0.1"
+default_parameters = [
+    default_max_new_tokens,
+    default_temperature,
+    default_repetition_penalty,
+    default_top_k,
+    default_top_p,
+]
+
+# Examples
+examples = [
+    [
+        "Bonjour Claire. Quel est votre sport préféré?",  # user_message
+        False,
+        "",  # bot_message_start
+        # "",  # First name
+        *default_parameters,
+    ],
+    [
+        "Bonjour. Je vous propose de faire un tour de table.",  # user_message
+        True, # more than one turn
+        "",  # bot_message_start
+        # "",  # First name
+        *default_parameters,
+    ],
+    [
+        "Que vas-tu nous cuisiner aujourd'hui?",  # user_message
+        False,
+        "Alors, nous allons voir la recette",  # bot_message_start
+        # "", # First name
+        *default_parameters,
+    ],
+]
+
+# Override default gradio buttons
+gradio_buttons = dict(
+    submit_btn=gr.Button("Envoyer"), # Sumbit
+    retry_btn=gr.Button("🔄  Générer une autre réponse"), # "🔄  Retry"
+    undo_btn=gr.Button("↩️ Annuler"), # "↩️ Undo"
+    clear_btn=gr.Button("🗑️  Effacer la conversation"), # "🗑️  Clear"
+    # stop_btn= None,
+    stop_btn=gr.Button("Arrêter"), # Stop
+)
+additional_inputs_name="Paramètres" # "Additional inputs"
+textbox=gr.Textbox(
+    container=False,
+    show_label=False,
+    label="Message",
+    placeholder="Votre message (laissez vide pour que le Bot continue seul)...",
+    scale=7,
+    lines=2,
+    autofocus=False,
+)
+chatbot_label="Conversation" # Chatbot
+
+
+additional_inputs = [
+    gr.Checkbox(
+        False,
+        label="Plus qu'un tour de parole",
+        info="Générer plusieurs tours de parole (et donc comment vous pourriez continuer la conversation)",
+    ),
+    gr.Textbox(
+        "",
+        label="Début de réponse",
+        info="Vous pouvez taper ici ce que commence à vous répondre le Bot (pensez à actualiser entre chaque génération)",
+        type="text",
+    ),
+    # gr.Textbox(
+    #     "",
+    #     label="Votre prénom",
+    #     info="Prénom de vous en tant qu'interlocuteur (si vous vous nommez, le bot s'appellera Claire)",
+    # ),
+    gr.Slider(
+        label="Longueur max",
+        info="Longueur maximale du texte généré (en nombre de 'tokens' ~ mots et ponctuations)",
+        value=default_max_new_tokens,
+        minimum=25,
+        maximum=1000,
+        step=25,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Température",
+        info="Une valeur élevée augmente la diversité du texte généré, mais peut aussi produire des résultats incohérents",
+        value=default_temperature,
+        minimum=0.1,
+        maximum=1.9,
+        step=0.1,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Pénalité de répétition",
+        info="Pénalisation des répétitions",
+        value=default_repetition_penalty,
+        minimum=1.0,
+        maximum=1.95,
+        step=0.05,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Top-k",
+        info="Une valeur élevée permet d'explorer plus d'alternatives",
+        value=default_top_k,
+        minimum=1,
+        maximum=50,
+        step=1,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Top-p",
+        info="Une valeur élevée permet d'explorer plus d'alternatives",
+        value=default_top_p,
+        minimum=0.9,
+        maximum=1.0,
+        step=0.01,
+        interactive=True,
+    ),
+]
+
+STREAMING = True
 
 print("Loading model...")
 
@@ -38,171 +169,134 @@ newspk_token_id = tokenizer.encode("[")
 assert len(newspk_token_id) == 1
 newspk_token_id = newspk_token_id[0]
 
-
-# Class to encapsulate the Claire chatbot
-class ClaireChatBot:
-    def __init__(
-        self,
-        # Chat will display...
-        user_name="VOUS:",
-        bot_name="CHATBOT:",
-        other_name_regex_in=r"AUTRE (\d+):",
-        other_name_regex_out=r"AUTRE \1:",
-        # but Claire was trained on...
-        user_internal_tag="[Intervenant 1:]",
-        bot_internal_tag="[Intervenant 2:]",
-        other_internal_tag_regex_in=r"\[Intervenant (\d+):\]",
-        other_internal_tag_regex_out=r"\[Intervenant \1:\]",
-    ):
-        self.user_name = user_name
-        self.bot_name = bot_name
-        self.other_name_regex_in = other_name_regex_in
-        self.other_name_regex_out = other_name_regex_out
-
-        self.user_internal_tag = user_internal_tag
-        self.bot_internal_tag = bot_internal_tag
-        self.other_internal_tag_regex_in = other_internal_tag_regex_in
-        self.other_internal_tag_regex_out = other_internal_tag_regex_out
-
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        self.has_started_bracket = False
-        self.history = ""
-        self.history_raw = ""
-        self.reinject_history = False
-        self.reshow_history = False
-
-    def predict(
-        self,
-        user_message,
-        bot_message_start="",
-        conversation_history="",
-        generate_several_turns=False,
-        max_new_tokens=default_max_new_tokens,
-        temperature=default_temperature,
-        top_k=default_top_k,
-        top_p=default_top_p,
-        repetition_penalty=default_repetition_penalty,
-    ):
-        user_message = claire_text_preproc_message(user_message)
-        bot_message_start = claire_text_preproc_message(bot_message_start)
-
-        if conversation_history:
-            # Format conversation history
-            for spk_in, spk_out in [
-                (self.user_name, self.user_internal_tag),
-                (self.bot_name, self.bot_internal_tag),
-            ]:
-                conversation_history = conversation_history.replace(spk_in, spk_out)
-            conversation_history = re.sub(self.other_name_regex_in, self.other_internal_tag_regex_out, conversation_history)
-            conversation_history = claire_text_preproc_conversation(conversation_history)
-            conversation_history = conversation_history.rstrip() + "\n"
-        else:
-            conversation_history = self.history_raw
-
-        # (Only relevant if self.reinject_history is True)
-        user_internal_tag = self.user_internal_tag
-        if self.has_started_bracket:
-            user_internal_tag = user_internal_tag[1:]
-
-        # Combine the user and bot messages into a conversation
-        conversation = f"{conversation_history}{user_internal_tag} {user_message}\n{self.bot_internal_tag} {bot_message_start if bot_message_start else ''}".strip()
-
-        # Encode the conversation using the tokenizer
-        input_ids = tokenizer.encode(
-            conversation, return_tensors="pt", add_special_tokens=False
+tokenizer.add_special_tokens({"eos_token": "["})
+
+user_internal_tag = "[Intervenant 1:]"
+bot_internal_tag = "[Intervenant 2:]"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+@spaces.GPU
+def generate(
+    user_message,
+    conversation_history=[],
+    generate_several_turns=False,
+    bot_message_start="",
+    # user_surname="",
+    max_new_tokens=default_max_new_tokens,
+    temperature=default_temperature,
+    repetition_penalty=default_repetition_penalty,
+    top_k=default_top_k,
+    top_p=default_top_p,
+    user_surname="", # Experimental (TODO)
+    remove_unfinished_sentence=True,
+):
+    user_message = claire_text_preproc_message(user_message)
+    bot_message_start = claire_text_preproc_message(bot_message_start)
+
+    if user_surname:
+        user_surname = capitalize(collapse_whitespaces(re.sub(r"[^\p{L}\-\.']", " ", user_surname))).strip()
+    if user_surname:
+        user_tag = f"[{user_surname}:]"
+        bot_tag = f"[Claire:]"
+    else:
+        user_tag = user_internal_tag
+        bot_tag = bot_internal_tag
+
+    if conversation_history:
+        conversation_history = "\n".join(
+            [
+                f"{user_tag} {claire_text_preproc_message(user)}\n{bot_tag} {claire_text_preproc_message(bot) if bot else ''}"
+                for user, bot in conversation_history
+            ]
         )
-        input_ids = input_ids.to(self.device)
-
-        # Generate a response using Claire
-        response = model.generate(
-            input_ids=input_ids,
-            use_cache=False,
-            early_stopping=False,
-            temperature=temperature,
-            do_sample=True,
-            max_new_tokens=max_new_tokens,
-            top_k=top_k,
-            top_p=top_p,
-            repetition_penalty=repetition_penalty,
-            pad_token_id=eos_token_id,
-            eos_token_id=eos_token_id if generate_several_turns else newspk_token_id,
+        conversation_history = from_display_to_internal(conversation_history)
+        conversation_history = conversation_history.rstrip()
+        if conversation_history:
+            conversation_history += "\n"
+    else:
+        conversation_history = ""
+    if not bot_message_start:
+        bot_message_start = ""
+
+    # Combine the user and bot messages into a conversation
+    conversation = f"{conversation_history}{user_tag} {user_message}\n{bot_tag} {bot_message_start}".strip()
+    conversation = remove_empty_turns(conversation)
+
+    # Encode the conversation using the tokenizer
+    input_ids = tokenizer.encode(
+        conversation, return_tensors="pt", add_special_tokens=True
+    )
+    input_ids = input_ids.to(device)
+
+    skip_special_tokens = not generate_several_turns
+
+    if STREAMING:
+        streamer = transformers.TextIteratorStreamer(
+            tokenizer,
+            timeout=10.0,
+            skip_prompt=True,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=False,
         )
-
-        # Decode the generated response to text
-        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
-
-        # Remove last unfinished speech turn/sentence/phrase
-        line_breaks = [u.span(0)[0] for u in re.finditer("\n", response_text)]
-        remove_last_sentence = True
-        if generate_several_turns:
-            if len(line_breaks) >= 2:
-                response_text = response_text[: line_breaks[-1]]
-                line_breaks.pop(-1)
-                remove_last_sentence = False
-        if remove_last_sentence and len(line_breaks) == 1:
-            sentence_ends = [
-                u.span(0)[0] for u in re.finditer(r"[\.!?]", response_text)
-            ]
-            sentence_ends = [p for p in sentence_ends if p > line_breaks[-1]]
-            if sentence_ends:
-                response_text = response_text[: sentence_ends[-1] + 1]
-            else:
-                phrase_ends = [
-                    u.span(0)[0] for u in re.finditer(r"[,;]", response_text)
-                ]
-                phrase_ends = [p for p in phrase_ends if p > line_breaks[-1]]
-                if phrase_ends:
-                    response_text = response_text[: phrase_ends[-1] + 1]
-
-        ended_with_bracket = response_text.endswith("[")
-
-        if self.reinject_history:
-            self.history_raw = response_text
-            self.has_started_bracket = ended_with_bracket
-
-        if ended_with_bracket:
-            response_text = response_text[:-1]
-
-        for spk_in, spk_out in [
-            (self.user_internal_tag, self.user_name),
-            (self.user_internal_tag[1:], self.user_name), # Starting bracket may be missing  
-            (self.bot_internal_tag, self.bot_name),
-        ]:
-            response_text = response_text.replace(spk_in, spk_out)
-        response_text = re.sub(self.other_internal_tag_regex_in, self.other_name_regex_out, response_text)
-
-        if self.reshow_history:
-            previous_history = self.history
-            self.history = previous_history + response_text + "\n"
+    else:
+        streamer = None
+
+    # Generation parameters
+    generate_kwargs = dict(
+        input_ids=input_ids,
+        streamer=streamer,
+        eos_token_id=eos_token_id if generate_several_turns else newspk_token_id,
+        pad_token_id=eos_token_id,
+        do_sample=True,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        repetition_penalty=repetition_penalty,
+        top_k=top_k,
+        top_p=top_p,
+        num_beams=1,
+        # use_cache=False,
+        # early_stopping=False,
+    )
+    if STREAMING:
+        t = Thread(target=model.generate, kwargs=generate_kwargs)
+        t.start()
+
+        outputs = []
+        if bot_message_start.strip():
+            yield bot_message_start
+        for token in streamer:
+            # Ignore line breaks
+            if not generate_several_turns and re.match(r"\s*\n$", token):
+                continue
+            outputs.append(token)
+            text = bot_message_start + from_internal_to_display("".join(outputs))
+            yield text
+    else:
+        output_ids = model.generate(**generate_kwargs)
+        output_ids = output_ids[0][len(input_ids[0]) :]
+        text = tokenizer.decode(output_ids, skip_special_tokens=skip_special_tokens)
+        if bot_message_start.strip():
+            bot_message_start = bot_message_start.strip() + " "
+
+        text = bot_message_start + from_internal_to_display(text.rstrip("[").strip())
+        yield text
+
+    if generate_several_turns:
+        if remove_unfinished_sentence:
+            yield remove_last_unfinished_sentence(text)
         else:
-            previous_history = ""
-
-        return previous_history + response_text
-
-
-def claire_text_preproc_conversation(text):
-    text = format_special_characters(text)
-    text = collapse_whitespaces_conversations(text)
-    return text
+            yield remove_last_unfinished_turn(text)[0]
 
 
 def claire_text_preproc_message(text):
     text = format_special_characters(text)
-    text = collapse_whitespaces_message(text)
+    text = collapse_whitespaces(text)
     text = replace_brackets(text)
     return text
 
 
-def collapse_whitespaces_conversations(text):
-    text = re.sub(r"\n+", "\n", text)
-    text = re.sub(r"[ \t]+", " ", text)
-    text = re.sub(r"\n ", "\n", text)
-    text = re.sub(r" ([\.,])", r"\1", text)
-    return text.lstrip().rstrip(" ")
-
-
-def collapse_whitespaces_message(text):
+def collapse_whitespaces(text):
     text = re.sub(r"\s+", " ", text)
     text = re.sub(r" ([\.,])", r"\1", text)
     return text.lstrip().rstrip(" ")
@@ -235,184 +329,102 @@ def format_special_characters(text):
     return text
 
 
-# Create the Claire chatbot instance
-chatbot = ClaireChatBot()
+user_name = "[Vous:]"
+bot_name = "[Bot:]"
 
-# Define the Gradio interface
-title = "Démo de conversation avec Claire"
-description = "Simulation de conversations en Français avec [Claire](https://huggingface.co/OpenLLM-France/Claire-7B-0.1), sans recherche de vérité, et avec potentiellement un peu d'humour."
 
-default_parameters = [
-    default_temperature,
-    default_top_k,
-    default_top_p,
-    default_repetition_penalty,
-]
+def from_internal_to_display(text):
+    for before, after in [
+        (user_internal_tag, user_name),
+        (bot_internal_tag, bot_name),
+    ]:
+        text = text.replace(before, after)
+    return text
 
-examples = [
-    [
-        "Nous allons commencer cette interview avec une question un peu classique. Quel est votre sport préféré?",  # user_message
-        "",  # bot_message_start
-        "",  # conversation_history
-        True,  # generate_several_turns
-        200,  # max_new_tokens
-        *default_parameters,
-    ],
-    [
-        "Que vas-tu nous cuisiner aujourd'hui?",  # user_message
-        "Alors, nous allons voir la recette de",  # bot_message_start
-        "VOUS: Bonjour Claire.\nCHATBOT: Bonjour Dominique.",  # conversation_history
-        False,  # generate_several_turns
-        default_max_new_tokens,  # max_new_tokens
-        *default_parameters,
-    ],
-]
 
-# # Test
-# chatbot.predict(*examples[0])
+def from_display_to_internal(text):
+    for before, after in [
+        (user_name, user_internal_tag),
+        (bot_name, bot_internal_tag),
+    ]:
+        text = text.replace(before, after)
+    return text
 
-inputs = [
-    gr.Textbox(
-        "",
-        label="Prompt",
-        info="Tapez ce que vous voulez dire au ChatBot",
-        type="text",
-        lines=2,
-    ),
-    gr.Textbox(
-        "",
-        label="Début de réponse",
-        info="Vous pouvez taper ici ce que commence à vous répondre le ChatBot",
-        type="text",
-    ),
-    gr.Textbox(
-        "",
-        label="Historique",
-        info="Vous pouvez copier-coller (et modifier?) ici votre historique de conversation, pour continuer cette conversation",
-        type="text",
-        lines=3,
-    ),
-    gr.Checkbox(
-        False,
-        label="Plus qu'un tour de parole",
-        info="Générer aussi comment pourrait continuer la conversation (plusieurs tours de parole incluant le vôtre)",
-    ),
-    gr.Slider(
-        label="Longueur max",
-        info="Longueur maximale du texte généré (en nombre de 'tokens' ~ mots et ponctuations)",
-        value=default_max_new_tokens,
-        minimum=25,
-        maximum=1000,
-        step=25,
-        interactive=True,
-    ),
-    gr.Slider(
-        label="Température",
-        info="Une valeur élevée augmente la diversité du texte généré, mais peut aussi produire des résultats incohérents",
-        value=default_temperature,
-        minimum=0.1,
-        maximum=1.9,
-        step=0.1,
-        interactive=True,
-    ),
-    gr.Slider(
-        label="Top-k",
-        info="Une valeur élevée permet d'explorer plus d'alternatives, mais augmente les temps de calcul",
-        value=default_top_k,
-        minimum=1,
-        maximum=50,
-        step=1,
-        interactive=True,
-    ),
-    gr.Slider(
-        label="Top-p",
-        info="Une valeur élevée permet d'explorer des alternatives moins probables",
-        value=default_top_p,
-        minimum=0.9,
-        maximum=1.0,
-        step=0.01,
-        interactive=True,
-    ),
-    gr.Slider(
-        label="Pénalité de répétition",
-        info="Pénalisation des répétitions",
-        value=default_repetition_penalty,
-        minimum=1.0,
-        maximum=2.0,
-        step=0.05,
-        interactive=True,
-    ),
-]
 
-theme = gr.themes.Monochrome(
-    secondary_hue="emerald",
-    neutral_hue="teal",
-).set(
-    body_background_fill="*primary_950",
-    body_background_fill_dark="*secondary_950",
-    body_text_color="*primary_50",
-    body_text_color_dark="*secondary_100",
-    body_text_color_subdued="*primary_300",
-    body_text_color_subdued_dark="*primary_300",
-    background_fill_primary="*primary_600",
-    background_fill_primary_dark="*primary_400",
-    background_fill_secondary="*primary_950",
-    background_fill_secondary_dark="*primary_950",
-    border_color_accent="*secondary_600",
-    border_color_primary="*secondary_50",
-    border_color_primary_dark="*secondary_50",
-    color_accent="*secondary_50",
-    color_accent_soft="*primary_500",
-    color_accent_soft_dark="*primary_500",
-    link_text_color="*secondary_950",
-    link_text_color_dark="*primary_50",
-    link_text_color_active="*primary_50",
-    link_text_color_active_dark="*primary_50",
-    link_text_color_hover="*primary_50",
-    link_text_color_hover_dark="*primary_50",
-    link_text_color_visited="*primary_50",
-    block_background_fill="*primary_950",
-    block_background_fill_dark="*primary_950",
-    block_border_color="*secondary_500",
-    block_border_color_dark="*secondary_500",
-    block_info_text_color="*primary_50",
-    block_info_text_color_dark="*primary_50",
-    block_label_background_fill="*primary_950",
-    block_label_background_fill_dark="*secondary_950",
-    block_label_border_color="*secondary_500",
-    block_label_border_color_dark="*secondary_500",
-    block_label_text_color="*secondary_500",
-    block_label_text_color_dark="*secondary_500",
-    block_title_background_fill="*primary_950",
-    panel_background_fill="*primary_950",
-    panel_border_color="*primary_950",
-    checkbox_background_color="*primary_950",
-    checkbox_background_color_dark="*primary_950",
-    checkbox_background_color_focus="*primary_950",
-    checkbox_border_color="*secondary_500",
-    input_background_fill="*primary_800",
-    input_background_fill_focus="*primary_950",
-    input_background_fill_hover="*secondary_950",
-    input_placeholder_color="*secondary_950",
-    slider_color="*primary_950",
-    slider_color_dark="*primary_950",
-    table_even_background_fill="*primary_800",
-    table_odd_background_fill="*primary_600",
-    button_primary_background_fill="*primary_800",
-    button_primary_background_fill_dark="*primary_800",
-)
+def remove_last_unfinished_sentence(text):
+    text, removed_turn = remove_last_unfinished_turn(text)
+    if removed_turn:
+        return text
+    line_breaks = [u.span(0)[0] for u in re.finditer("\n", text)]
+    remove_last_sentence = True
+    if len(line_breaks) >= 1 and len(text[line_breaks[-1]:].split("]")[-1]) < 15:
+        text = text[: line_breaks[-1]]
+        line_breaks.pop(-1)
+        remove_last_sentence = False
+    if remove_last_sentence and len(line_breaks) >= 1:
+        sentence_ends = [u.span(0)[0] for u in re.finditer(r"[\.!?]", text)]
+        sentence_ends = [p for p in sentence_ends if p > line_breaks[-1]]
+        if sentence_ends:
+            text = text[: sentence_ends[-1] + 1]
+        else:
+            phrase_ends = [u.span(0)[0] for u in re.finditer(r"[,;]", text)]
+            phrase_ends = [p for p in phrase_ends if p > line_breaks[-1]]
+            if phrase_ends:
+                text = text[: phrase_ends[-1] + 1]
+    return text
 
-iface = gr.Interface(
-    fn=chatbot.predict,
+
+def remove_last_unfinished_turn(text):
+    starts = [u.span(0)[0] for u in re.finditer(r"\[", text)]
+    did_it = False
+    if starts and "]" not in text[starts[-1] :]:
+        text = text[: starts[-1]]
+        did_it = True
+    return text.rstrip(), did_it
+
+
+def remove_empty_turns(text):
+    while re.search(_empty_turn, text):
+        # Remove empty turns
+        text = re.sub(_empty_turn, r"\1", text)
+        # Remove same speaker speaking twice
+        text = re.sub(_repeated_turn, r"\1 \2", text)
+    return text
+
+_speaker_regex = r"\[[^\]]+:\]"
+_empty_turn = re.compile(_speaker_regex + r"[^\p{L}]*" + "(" + _speaker_regex + ")")
+_repeated_turn = re.compile(r"(" + _speaker_regex + r") ([^\[]*)\s\1")
+
+
+def capitalize(text):
+    # michel JR claude-marie -> Michel JR Claude-Marie
+    words = text.split(" ")
+    words = [w.capitalize() if (not w.isupper() or len(w)>2) else w for w in words]
+    for i, w in enumerate(words):
+        for sep in "-", "'":
+            if sep in w:
+                words[i] = sep.join([x.capitalize() if not x.isupper() else x for x in w.split(sep)])
+    return " ".join(words)
+
+# # Test
+# list(generate(*(examples[0][:1] + [[]] + examples[0][1:])))
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
     title=title,
     description=description,
+    chatbot=gr.Chatbot(label=chatbot_label),
+    textbox=textbox,
     examples=examples,
-    inputs=inputs,
-    outputs="text",
-    theme=theme,
+    additional_inputs=additional_inputs,
+    additional_inputs_accordion_name=additional_inputs_name,
+    autofocus=False,
+    **gradio_buttons,
 )
 
-print("Launching chat...")
-
-# Launch the Gradio interface for the model
-iface.launch(share=True)
+if __name__ == "__main__":
+    print("Launching chat...")
+    with gr.Blocks(css="style.css") as demo:
+        chat_interface.render()
+        demo.queue(max_size=20).launch()

requirements.txt

@@ -1,11 +1,11 @@
-optimum
 accelerate==0.24.1
 bitsandbytes==0.41.1
-gradio==4.1.1
+gradio==4.4.1
+optimum==1.14.1
 protobuf==3.20.3
 scipy==1.11.2
 sentencepiece==0.1.99
-spaces==0.18.0
-torch==2.0.0
+#spaces==0.18.0
+spaces
+torch==2.0.1
 transformers==4.35.0
-transformers_stream_generator==0.0.4

style.css

@@ -0,0 +1,9 @@
+/* h1 {
+    text-align: center;
+}
+
+.contain {
+    max-width: 900px;
+    margin: auto;
+    padding-top: 1.5rem;
+} */