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app (22).py

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+import os
+import gradio as gr
+from text_generation import Client, InferenceAPIClient
+
+cntxt = (
+    "\nUser: Hi!\nAssistant: I'm Mistral, a 7 Billion token model checkpoint, my version is 0.1, part of a larger set of checkpoints "
+    "trained by MistralAI! I was created to be an excellent expert assistant capable of carefully, logically, truthfully, methodically fulfilling any User request."
+    "I'm capable of acting as an expert AI Writing model, acting as an expert AI Programming model, acting as an expert AI Medical model, acting as an expert AI Legal model and much more... "
+    "I'm programmed to be helpful, polite, honest, and friendly.\n"
+)
+
+def get_client(model: str):
+    return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None))
+
+def get_usernames(model: str):
+    """
+    Returns:
+        (str, str, str, str): pre-prompt, username, bot name, separator
+    """
+    return cntxt, "User: ", "Assistant: ", "\n"
+
+def predict(model: str,inputs: str,typical_p: float,top_p: float,temperature: float,top_k: int,repetition_penalty: float,watermark: bool,chatbot,history,):
+    client = get_client(model)
+    preprompt, user_name, assistant_name, sep = get_usernames(model)
+    history.append(inputs)
+    past = []
+    for data in chatbot:
+        user_data, model_data = data
+        if not user_data.startswith(user_name):
+            user_data = user_name + user_data
+        if not model_data.startswith(sep + assistant_name):
+            model_data = sep + assistant_name + model_data
+        past.append(user_data + model_data.rstrip() + sep)
+
+    if not inputs.startswith(user_name):
+        inputs = user_name + inputs
+
+    total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()
+    partial_words = ""
+
+    if model in ("mistralai/Mistral-7B-v0.1", "mistralai/Mistral-7B-v0.1"):
+        iterator = client.generate_stream(total_inputs,typical_p=typical_p,truncate=1024,watermark=False,max_new_tokens=1024,)
+
+    for i, response in enumerate(iterator):
+        if response.token.special:
+            continue
+
+        partial_words = partial_words + response.token.text
+        if partial_words.endswith(user_name.rstrip()):
+            partial_words = partial_words.rstrip(user_name.rstrip())
+        if partial_words.endswith(assistant_name.rstrip()):
+            partial_words = partial_words.rstrip(assistant_name.rstrip())
+
+        if i == 0:
+            history.append(" " + partial_words)
+        elif response.token.text not in user_name:
+            history[-1] = partial_words
+
+        chat = [
+            (history[i].strip(), history[i + 1].strip())
+            for i in range(0, len(history) - 1, 2)
+        ]
+        yield chat, history
+
+def reset_textbox():
+    return gr.update(value="")
+
+def radio_on_change(value: str,typical_p,top_p,top_k,temperature,repetition_penalty,watermark,):
+    if model in ("mistralai/Mistral-7B-v0.1", "mistralai/Mistral-7B-v0.1"):
+        typical_p = typical_p.update(value=0.2, visible=True)
+        top_p = top_p.update(visible=False)
+        top_k = top_k.update(visible=False)
+        temperature = temperature.update(visible=False)
+        repetition_penalty = repetition_penalty.update(visible=False)
+        watermark = watermark.update(False)
+    return (typical_p,top_p,top_k,temperature,repetition_penalty,watermark,)
+
+with gr.Blocks(
+    css="""#col_container {margin-left: auto; margin-right: auto;}
+                #chatbot {height: 520px; overflow: auto;}"""
+) as demo:
+    with gr.Column(elem_id="col_container"):
+        model = gr.Radio(value="mistralai/Mistral-7B-v0.1",choices=["mistralai/Mistral-7B-v0.1",],label="Model",visible=False,)
+        chatbot = gr.Chatbot(elem_id="chatbot")
+        inputs = gr.Textbox(placeholder="Hi there!", label="Type an input and press Enter")
+        state = gr.State([])
+        b1 = gr.Button()
+
+        with gr.Accordion("Parameters", open=False, visible=False):
+            typical_p = gr.Slider(minimum=-0,maximum=1.0,value=0.2,step=0.05,interactive=True,label="Typical P mass",)
+            top_p = gr.Slider(minimum=-0,maximum=1.0,value=0.25,step=0.05,interactive=True,label="Top-p (nucleus sampling)",visible=False,)
+            temperature = gr.Slider(minimum=-0,maximum=5.0,value=0.6,step=0.1,interactive=True,label="Temperature",visible=False,)
+            top_k = gr.Slider(minimum=1,maximum=50,value=50,step=1,interactive=True,label="Top-k",visible=False,)
+            repetition_penalty = gr.Slider(minimum=0.1,maximum=3.0,value=1.03,step=0.01,interactive=True,label="Repetition Penalty",visible=False,)
+            watermark = gr.Checkbox(value=False, label="Text watermarking")
+
+    model.change(lambda value: radio_on_change(value,typical_p,top_p,top_k,temperature,repetition_penalty,watermark,),inputs=model,outputs=[typical_p,top_p,top_k,temperature,repetition_penalty,watermark,],)
+    inputs.submit(predict,[model,inputs,typical_p,top_p,temperature,top_k,repetition_penalty,watermark,chatbot,state,], [chatbot, state],)
+    b1.click(predict,[model,inputs,typical_p,top_p,temperature,top_k,repetition_penalty,watermark,chatbot,state,], [chatbot, state],)
+    b1.click(reset_textbox, [], [inputs])
+    inputs.submit(reset_textbox, [], [inputs])
+
+    demo.queue(max_size=1).launch(max_threads=1,)

requirements (76).txt

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+text-generation==0.5.0
+gradio==3.20.1