Update app.py

app.py

@@ -1,64 +1,98 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 import random
+import textwrap
 
-# Initialize the model
+# Define the model to be used
 model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
 client = InferenceClient(model)
 
-def chat_response(prompt, history, seed, temp, tokens, top_p, rep_p):
-    generate_kwargs = {
-        "temperature": temp,
-        "max_new_tokens": tokens,
-        "top_p": top_p,
-        "repetition_penalty": rep_p,
-        "do_sample": True,
-        "seed": seed,
-    }
-
-    # Include the chat history in the prompt
-    formatted_prompt = "\n".join([f"Q: {user_prompt}\nA: {bot_response}" for user_prompt, bot_response in history]) + f"\nQ: {prompt}\nA:"
-    
-    output = ""
-    
-    # Generating text in streaming mode
-    for response in client.text_generation(formatted_prompt, **generate_kwargs, stream=True):
-        # Assuming response is directly a string or contains a message
-        output += response  # Using response directly since it's a string
+# Embedded system prompt
+system_prompt_text = "You are a smart and helpful co-worker of Thailand based multi-national company PTT, and PTTEP. You help with any kind of request and provide a detailed answer to the question. But if you are asked about something unethical or dangerous, you must refuse and provide a safe and respectful way to handle that."
 
-        # Yield the updated output for real-time display
-        yield [(prompt, output)]
+# Read the content of the info.md file
+with open("info.md", "r") as file:
+    info_md_content = file.read()
 
-    # Append the full response to history after completion
-    history.append((prompt, output))
-    yield history  # Yielding the updated history
-
-def clear_chat():
-    return [], []  # Returning an empty history
-
-# Gradio interface
-with gr.Blocks() as app:
-    gr.HTML("<center><h1>Chatbot</h1><h3>Ask your questions!</h3></center>")
-    
-    chat_box = gr.Chatbot(height=500)
-    inp = gr.Textbox(label="Your Question", lines=5)
-    btn = gr.Button("Ask")
-    clear_btn = gr.Button("Clear")
-    
-    rand_seed = gr.Checkbox(label="Random Seed", value=True)
-    seed_slider = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
-    tokens_slider = gr.Slider(label="Max new tokens", value=3840, minimum=0, maximum=8000)
-    temp_slider = gr.Slider(label="Temperature", value=0.9, minimum=0.01, maximum=1.0)
-    top_p_slider = gr.Slider(label="Top-P", value=0.9, minimum=0.01, maximum=1.0)
-    rep_p_slider = gr.Slider(label="Repetition Penalty", value=1.0, minimum=0.1, maximum=2.0)
-
-    # Handle button click to get chat response
-    btn.click(
-        lambda prompt: chat_response(prompt, [], seed_slider.value, temp_slider.value, tokens_slider.value, top_p_slider.value, rep_p_slider.value),
-        inp,
-        chat_box,
+# Chunk the info.md content into smaller sections
+chunk_size = 2500  # Adjust this size as needed
+info_md_chunks = textwrap.wrap(info_md_content, chunk_size)
+
+def get_all_chunks(chunks):
+    return "\n\n".join(chunks)
+
+def format_prompt_mixtral(message, history, info_md_chunks):
+    prompt = "<s>"
+    all_chunks = get_all_chunks(info_md_chunks)
+    prompt += f"{all_chunks}\n\n"  # Add all chunks of info.md at the beginning
+    prompt += f"{system_prompt_text}\n\n"  # Add the system prompt
+
+    if history:
+        for user_prompt, bot_response in history:
+            prompt += f"[INST] {user_prompt} [/INST]"
+            prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def chat_inf(prompt, history, seed, temp, tokens, top_p, rep_p):
+    generate_kwargs = dict(
+        temperature=temp,
+        max_new_tokens=tokens,
+        top_p=top_p,
+        repetition_penalty=rep_p,
+        do_sample=True,
+        seed=seed,
     )
 
-    clear_btn.click(clear_chat, None, [inp, chat_box])
+    formatted_prompt = format_prompt_mixtral(prompt, history, info_md_chunks)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield [(prompt, output)]
+    history.append((prompt, output))
+    yield history
+
+def clear_fn():
+    return None, None
+
+rand_val = random.randint(1, 1111111111111111)
+
+def check_rand(inp, val):
+    if inp:
+        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
+    else:
+        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
+
+with gr.Blocks() as app:  # Add auth here
+    gr.HTML("""<center><h1 style='font-size:xx-large;'>PTT Chatbot</h1><br><h3>running on Huggingface Inference </h3><br><h7>EXPERIMENTAL</center>""")
+    with gr.Row():
+        chat = gr.Chatbot(height=500)
+    with gr.Group():
+        with gr.Row():
+            with gr.Column(scale=3):
+                inp = gr.Textbox(label="Prompt", lines=5, interactive=True)  # Increased lines and interactive
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        btn = gr.Button("Chat")
+                    with gr.Column(scale=1):
+                        with gr.Group():
+                            stop_btn = gr.Button("Stop")
+                            clear_btn = gr.Button("Clear")
+            with gr.Column(scale=1):
+                with gr.Group():
+                    rand = gr.Checkbox(label="Random Seed", value=True)
+                    seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
+                    tokens = gr.Slider(label="Max new tokens", value=3840, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens")
+                    temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+                    top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+                    rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
+
+    hid1 = gr.Number(value=1, visible=False)
+
+    go = btn.click(check_rand, [rand, seed], seed).then(chat_inf, [inp, chat, seed, temp, tokens, top_p, rep_p], chat)
+
+    stop_btn.click(None, None, None, cancels=[go])
+    clear_btn.click(clear_fn, None, [inp, chat])
 
-app.launch(share=True, auth=("admin", "0112358"))
+app.queue(default_concurrency_limit=10).launch(share=True, auth=("admin", "0112358"))