Update app.py

app.py

@@ -1,8 +1,133 @@
+import os
+import logging
+
+from huggingface_hub import InferenceClient
 from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+log_level = os.environ.get("LOG_LEVEL", "WARNING")
+logging.basicConfig(encoding='utf-8', level=log_level)
 
-tokenizer = AutoTokenizer.from_pretrained(
+logging.info("Creating Inference Client")
+client = InferenceClient(
     "cognitivecomputations/dolphin-2.6-mistral-7b"
 )
-model = AutoModelForCausalLM.from_pretrained(
-    "cognitivecomputations/dolphin-2.6-mistral-7b"
-)
+
+def format_prompt(message, history):
+    """Formats the prompt for the AI"""
+    logging.info("Formatting Prompt")
+    logging.debug("Input Message: %s", message)
+    logging.debug("Input History: %s", history)
+
+    prompt = "<|im_start|>system\n" +\
+        "You are Dolphin, a helpful AI assistant.<|im_end|>"
+    prompt += "<|im_start|>user\n" + f"{message}<|im_end|>"
+    prompt += "<|im_start|>assistant"
+
+    return prompt
+
+
+def generate(
+    prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+):
+    logging.info("Generating Response")
+    logging.debug("Input Prompt: %s", prompt)
+    logging.debug("Input History: %s", history)
+    logging.debug("Input System Prompt: %s", system_prompt)
+    logging.debug("Input Temperature: %s", temperature)
+    logging.debug("Input Max New Tokens: %s", max_new_tokens)
+    logging.debug("Input Top P: %s", top_p)
+    logging.debug("Input Repetition Penalty: %s", repetition_penalty)
+
+    logging.info("Converting Parameters to Correct Type")
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+    logging.debug("Temperature: %s", temperature)
+    logging.debug("Top P: %s", top_p)
+
+    logging.info("Creating Generate kwargs")
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+    logging.debug("Generate Args: %s", generate_kwargs)
+
+    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+    logging.debug("Prompt: %s", formatted_prompt)
+
+    logging.info("Generating Text")
+    stream = client.text_generation(
+        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+
+    logging.info("Creating Output")
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+
+    logging.debug("Output: %s", output)
+    return output
+
+
+additional_inputs = [
+    gr.Textbox(
+        label="System Prompt",
+        max_lines=1,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=256,
+        minimum=0,
+        maximum=1048,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.0,
+        maximum=1,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    )
+]
+
+examples = []
+
+logging.info("Creating Chat Interface")
+gr.ChatInterface(
+    fn=generate,
+    chatbot=gr.Chatbot(show_label=False, show_share_button=False,
+                       show_copy_button=True, likeable=True, layout="panel"),
+    additional_inputs=additional_inputs,
+    title="Dolphin Mistral",
+    examples=examples,
+    concurrency_limit=20,
+).launch(show_api=False)