Update app.py

app.py

@@ -1,3 +1,183 @@
-import gradio as gr
+import os
+import logging
+import streamlit as st
+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
+from llama_cpp_agent.prompt_templates import web_search_system_prompt, research_system_prompt
+from utils import CitingSources
+from settings import get_context_by_model, get_messages_formatter_type
 
-gr.load("models/mistralai/Mistral-7B-Instruct-v0.3").launch()
+# Install necessary libraries using os.system
+os.system("pip install streamlit llama-cpp-agent huggingface_hub")
+
+# Download the models
+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/Meta-Llama-3-8B-Instruct-GGUF",
+    filename="Meta-Llama-3-8B-Instruct-Q6_K.gguf",
+    local_dir="./models"
+)
+hf_hub_download(
+    repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF",
+    filename="mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf",
+    local_dir="./models"
+)
+
+# Function to respond to user messages
+def respond(message, history, model, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty):
+    chat_template = get_messages_formatter_type(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),
+    )
+    provider = LlamaCppPythonProvider(llm)
+    logging.info(f"Loaded chat examples: {chat_template}")
+    search_tool = WebSearchTool(
+        llm_provider=provider,
+        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
+
+# Streamlit app
+st.title("Llama-CPP-Agent Chatbot with Web Search")
+
+# Sidebar for settings
+st.sidebar.title("Settings")
+model = st.sidebar.selectbox(
+    "Model",
+    [
+        'Mistral-7B-Instruct-v0.3-Q6_K.gguf',
+        'mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf',
+        'Meta-Llama-3-8B-Instruct-Q6_K.gguf'
+    ]
+)
+system_message = st.sidebar.text_area("System message", value=web_search_system_prompt)
+max_tokens = st.sidebar.slider("Max tokens", min_value=1, max_value=4096, value=2048, step=1)
+temperature = st.sidebar.slider("Temperature", min_value=0.1, max_value=1.0, value=0.45, step=0.1)
+top_p = st.sidebar.slider("Top-p", min_value=0.1, max_value=1.0, value=0.95, step=0.05)
+top_k = st.sidebar.slider("Top-k", min_value=0, max_value=100, value=40, step=1)
+repeat_penalty = st.sidebar.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.1, step=0.1)
+
+# Chat history
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# Chat input
+message = st.text_input("You:", key="input")
+
+if st.button("Send"):
+    history = st.session_state.history
+    response = respond(
+        message,
+        history,
+        model,
+        system_message,
+        max_tokens,
+        temperature,
+        top_p,
+        top_k,
+        repeat_penalty
+    )
+    
+    for res in response:
+        st.session_state.history.append((message, res))
+        st.text_area("Chat", value=f"You: {message}\nBot: {res}", height=300)
+
+# Display chat history
+for user_msg, bot_msg in st.session_state.history:
+    st.text_area("Chat", value=f"You: {user_msg}\nBot: {bot_msg}", height=300)