Create app.py

app.py

@@ -0,0 +1,237 @@
+import spaces
+import logging
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+from llama_cpp import Llama
+from llama_cpp_agent.providers import LlamaCppPythonProvider
+from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
+from llama_cpp_agent.chat_history import BasicChatHistory
+from llama_cpp_agent.chat_history.messages import Roles
+from llama_cpp_agent.llm_output_settings import (
+    LlmStructuredOutputSettings,
+    LlmStructuredOutputType,
+)
+from llama_cpp_agent.tools import WebSearchTool, GoogleWebSearchProvider
+from llama_cpp_agent.prompt_templates import web_search_system_prompt, research_system_prompt
+from lib.ui import css, PLACEHOLDER
+from lib.utils import CitingSources
+from lib.settings import get_context_by_model, get_messages_formatter_type
+
+llm = None
+llm_model = None
+
+hf_hub_download(
+    repo_id="bartowski/Mistral-7B-Instruct-v0.3-GGUF",
+    filename="Mistral-7B-Instruct-v0.3-Q6_K.gguf",
+    local_dir="./models"
+)
+hf_hub_download(
+    repo_id="bartowski/cognitivecomputations_Dolphin3.0-Mistral-24B-GGUF",
+    filename="cognitivecomputations_Dolphin3.0-Mistral-24B-Q8_0.gguf",
+    local_dir = "./models"
+)
+hf_hub_download(
+    repo_id="bartowski/gemma-2-27b-it-GGUF",
+    filename="gemma-2-27b-it-Q8_0.gguf",
+    local_dir = "./models"
+)
+
+examples = [
+    ["latest news about Yann LeCun"],
+    ["Latest news site:github.blog"],
+    ["Where I can find best hotel in Galapagos, Ecuador intitle:hotel"],
+    ["filetype:pdf intitle:python"]
+]
+
+def write_message_to_user():
+    """
+    Let you write a message to the user.
+    """
+    return "Please write the message to the user."
+
+
+@spaces.GPU(duration=120)
+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}",
+            flash_attn=True,
+            n_gpu_layers=81,
+            n_batch=1024,
+            n_ctx=get_context_by_model(model),
+        )
+        llm_model = model
+    provider = LlamaCppPythonProvider(llm)
+    logging.info(f"Loaded chat examples: {chat_template}")
+    search_tool = WebSearchTool(
+        llm_provider=provider,
+        web_search_provider=GoogleWebSearchProvider(),
+        message_formatter_type=chat_template,
+        max_tokens_search_results=12000,
+        max_tokens_per_summary=2048,
+    )
+
+    web_search_agent = LlamaCppAgent(
+        provider,
+        system_prompt=web_search_system_prompt,
+        predefined_messages_formatter_type=chat_template,
+        debug_output=True,
+    )
+
+    answer_agent = LlamaCppAgent(
+        provider,
+        system_prompt=research_system_prompt,
+        predefined_messages_formatter_type=chat_template,
+        debug_output=True,
+    )
+
+    settings = provider.get_provider_default_settings()
+    settings.stream = False
+    settings.temperature = temperature
+    settings.top_k = top_k
+    settings.top_p = top_p
+
+    settings.max_tokens = max_tokens
+    settings.repeat_penalty = repeat_penalty
+
+    output_settings = LlmStructuredOutputSettings.from_functions(
+        [search_tool.get_tool()]
+    )
+
+    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)
+
+    result = web_search_agent.get_chat_response(
+        message,
+        llm_sampling_settings=settings,
+        structured_output_settings=output_settings,
+        add_message_to_chat_history=False,
+        add_response_to_chat_history=False,
+        print_output=False,
+    )
+
+    outputs = ""
+
+    settings.stream = True
+    response_text = answer_agent.get_chat_response(
+        f"Write a detailed and complete research document that fulfills the following user request: '{message}', based on the information from the web below.\n\n" +
+        result[0]["return_value"],
+        role=Roles.tool,
+        llm_sampling_settings=settings,
+        chat_history=messages,
+        returns_streaming_generator=True,
+        print_output=False,
+    )
+
+    for text in response_text:
+        outputs += text
+        yield outputs
+
+    output_settings = LlmStructuredOutputSettings.from_pydantic_models(
+        [CitingSources], LlmStructuredOutputType.object_instance
+    )
+
+    citing_sources = answer_agent.get_chat_response(
+        "Cite the sources you used in your response.",
+        role=Roles.tool,
+        llm_sampling_settings=settings,
+        chat_history=messages,
+        returns_streaming_generator=False,
+        structured_output_settings=output_settings,
+        print_output=False,
+    )
+    outputs += "\n\nSources:\n"
+    outputs += "\n".join(citing_sources.sources)
+    yield outputs
+
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Dropdown([
+            'cognitivecomputations_Dolphin3.0-Mistral-24B-Q8_0.gguf',
+            'Mistral-7B-Instruct-v0.3-Q6_K.gguf',
+            'gemma-2-27b-it-Q8_0.gguf'
+        ],
+            value="Mistral-7B-Instruct-v0.3-Q6_K.gguf",
+            label="Model"
+        ),
+        gr.Textbox(value=web_search_system_prompt, label="System message"),
+        gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.45, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p",
+        ),
+        gr.Slider(
+            minimum=0,
+            maximum=100,
+            value=40,
+            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="blue",
+        secondary_hue="blue",
+        neutral_hue="gray",
+        font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set(
+            body_background_fill_dark="#1f1f1f",
+            block_background_fill_dark="#1f1f1f",
+            block_border_width="1px",
+            block_title_background_fill_dark="#1f1f1f",
+            input_background_fill_dark="#202124",
+            button_secondary_background_fill_dark="#202124",
+            border_color_accent_dark="#3b3c3f",
+            border_color_primary_dark="#3b3c3f",
+            background_fill_secondary_dark="#1f1f1f",
+            color_accent_soft_dark="transparent",
+            code_background_fill_dark="#202124"
+        ),
+        css=css,
+        retry_btn="Retry",
+        undo_btn="Undo",
+        clear_btn="Clear",
+        submit_btn="Send",
+        cache_examples=False,
+        examples = (examples),
+        description="Llama-cpp-agent: Chat with Google Agent",
+        analytics_enabled=False,
+        chatbot=gr.Chatbot(
+            scale=1,
+            placeholder=PLACEHOLDER,
+            show_copy_button=True
+        )
+    )
+
+if __name__ == "__main__":
+    demo.launch()