Update app.py

app.py

@@ -1,188 +1,25 @@
-import json
-import subprocess
-import time
-from llama_cpp import Llama
-from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
-from llama_cpp_agent.providers import LlamaCppPythonProvider
-from llama_cpp_agent.chat_history import BasicChatHistory
-from llama_cpp_agent.chat_history.messages import Roles
-import gradio as gr
-from huggingface_hub import hf_hub_download
+from gradio_client import Client
 
-llm = None
-llm_model = None
+client = Client("KingNish/Llama-3.2-1b-CPU")
+result = client.predict(
+		message="Hello!!",
+		request="llama-3.2-1b-instruct-q4_k_m.gguf",
+		param_3="You are Meta Llama 3.2 (1B), an advanced AI assistant created by Meta. Your capabilities include:
 
-# Download the new model
-hf_hub_download(
-    repo_id="hugging-quants/Llama-3.2-1B-Instruct-Q4_K_M-GGUF",
-    filename="llama-3.2-1b-instruct-q4_k_m.gguf",
-    local_dir="./models"
-)
-
-def get_messages_formatter_type(model_name):
-    return MessagesFormatterType.LLAMA_3
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    model,
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    top_k,
-    repeat_penalty,
-):
-    global llm
-    global llm_model
-    
-    chat_template = get_messages_formatter_type(model)
-    
-    if llm is None or llm_model != model:
-        llm = Llama(
-            model_path=f"models/{model}",
-            n_gpu_layers=0,  # Adjust based on your GPU
-            n_batch=32398,     # Adjust based on your RAM
-            n_ctx=512,      # Adjust based on your RAM and desired context length
-        )
-        llm_model = model
-    
-    provider = LlamaCppPythonProvider(llm)
-
-    agent = LlamaCppAgent(
-        provider,
-        system_prompt=f"{system_message}",
-        predefined_messages_formatter_type=chat_template,
-        debug_output=True
-    )
-    
-    settings = provider.get_provider_default_settings()
-    settings.temperature = temperature
-    settings.top_k = top_k
-    settings.top_p = top_p
-    settings.max_tokens = max_tokens
-    settings.repeat_penalty = repeat_penalty
-    settings.stream = True
-
-    messages = BasicChatHistory()
-
-    for msn in history:
-        user = {
-            'role': Roles.user,
-            'content': msn[0]
-        }
-        assistant = {
-            'role': Roles.assistant,
-            'content': msn[1]
-        }
-        messages.add_message(user)
-        messages.add_message(assistant)
-    
-    start_time = time.time()
-    token_count = 0
-
-    stream = agent.get_chat_response(
-        message,
-        llm_sampling_settings=settings,
-        chat_history=messages,
-        returns_streaming_generator=True,
-        print_output=False
-    )
-    
-    outputs = ""
-    for output in stream:
-        outputs += output
-        token_count += len(output.split()) 
-        yield outputs
-
-    end_time = time.time()
-    latency = end_time - start_time
-    speed = token_count / (end_time - start_time)
-    print(f"Latency: {latency} seconds")
-    print(f"Speed: {speed} tokens/second")
-
-description = """<p><center>
-<a href="https://huggingface.co/hugging-quants/Llama-3.2-1B-Instruct-Q4_K_M-GGUF" target="_blank">[Meta Llama 3.2 (1B)]</a>
-Meta Llama 3.2 (1B) is a multilingual large language model (LLM) optimized for conversational dialogue use cases, including agentic retrieval and summarization tasks. It outperforms many open-source and closed chat models on industry benchmarks, and is intended for commercial and research use in multiple languages.
-</center></p>
-"""
-
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Dropdown([
-                "llama-3.2-1b-instruct-q4_k_m.gguf"
-            ],
-            value="llama-3.2-1b-instruct-q4_k_m.gguf",
-            label="Model"
-        ),
-        gr.TextArea(value="""You are Meta Llama 3.2 (1B), an advanced AI assistant created by Meta. Your capabilities include:
 1. Complex reasoning and problem-solving
 2. Multilingual understanding and generation
 3. Creative and analytical writing
 4. Code understanding and generation
 5. Task decomposition and step-by-step guidance
 6. Summarization and information extraction
+
 Always strive for accuracy, clarity, and helpfulness in your responses. If you're unsure about something, express your uncertainty. Use the following format for your responses:
-""", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=2.0,
-            value=0.9,
-            step=0.05,
-            label="Top-p",
-        ),
-        gr.Slider(
-            minimum=0,
-            maximum=100,
-            value=1,
-            step=1,
-            label="Top-k",
-        ),
-        gr.Slider(
-            minimum=0.0,
-            maximum=2.0,
-            value=1.1,
-            step=0.1,
-            label="Repetition penalty",
-        ),
-    ],
-    theme=gr.themes.Soft(primary_hue="violet", secondary_hue="violet", neutral_hue="gray",font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set(
-        body_background_fill_dark="#16141c",
-        block_background_fill_dark="#16141c",
-        block_border_width="1px",
-        block_title_background_fill_dark="#1e1c26",
-        input_background_fill_dark="#292733",
-        button_secondary_background_fill_dark="#24212b",
-        border_color_accent_dark="#343140",
-        border_color_primary_dark="#343140",
-        background_fill_secondary_dark="#16141c",
-        color_accent_soft_dark="transparent",
-        code_background_fill_dark="#292733",
-    ),
-    title="Meta Llama 3.2 (1B)",
-    description=description,
-    chatbot=gr.Chatbot(
-        scale=1, 
-        likeable=True,
-        show_copy_button=True
-    ),
-    examples=[
-        ["Hello! Can you introduce yourself?"],
-        ["What's the capital of France?"],
-        ["Can you explain the concept of photosynthesis?"],
-        ["Write a short story about a robot learning to paint."],
-        ["Explain the difference between machine learning and deep learning."],
-        ["Summarize the key points of climate change and its global impact."],
-        ["Explain quantum computing to a 10-year-old."],
-        ["Design a step-by-step meal plan for someone trying to lose weight and build muscle."]
-    ],
-    cache_examples=False,
-    autofocus=False,
-    concurrency_limit=None
+",
+		param_4=512,
+		param_5=0.7,
+		param_6=0.9,
+		param_7=1,
+		param_8=1.1,
+		api_name="/chat"
 )
-
-if __name__ == "__main__":
-    demo.launch()
+print(result)