app.py

import gradio as gr
import os
import time

# ------------------------------------------------------------------
# 1. Load Environment Variables
# ------------------------------------------------------------------
api_key = os.environ.get("GEMINI_API_KEY")
if not api_key:
    print(
        "Warning: GEMINI_API_KEY not found. Please set it in your Hugging Face Spaces Secrets."
    )

# ------------------------------------------------------------------
# 2. Initialize Backend
# ------------------------------------------------------------------
print("--- Starting FeiMatrix Synapse System ---")
try:
    from database.setup import initialize_system
    from core.agent import SmartAIAgent

    print("Core modules imported successfully.")

    registered_tools, tool_recommender = initialize_system()
    print("System database and tool recommender initialized successfully.")
    agent = SmartAIAgent(
        tool_recommender=tool_recommender,
        registered_tools=registered_tools,
        api_key=api_key,
    )
    print("AI Agent Core created successfully.")
except Exception as e:
    print(f"A critical error occurred during system initialization: {e}")
    agent = None

print("--- FeiMatrix Synapse is ready ---")


# ------------------------------------------------------------------
# 3. Gradio Event Handler Functions
# ------------------------------------------------------------------
def handle_user_message(user_input, history):
    if not user_input.strip():
        return "", history
    history.append([user_input, None])
    return "", history


def generate_bot_response(history):
    if agent is None:
        history[-1][
            1
        ] = "Sorry, the AI Assistant system failed to initialize and is currently unavailable."
        yield history
        return

    user_question = history[-1][0]
    bot_message = ""
    history[-1][1] = bot_message

    try:
        for chunk in agent.stream_run(user_question):
            bot_message += chunk
            history[-1][1] = bot_message
            yield history
            time.sleep(0.01)
    except Exception as e:
        error_message = f"\n\n**Sorry, an error occurred while processing your request:**\n```\n{type(e).__name__}: {str(e)}\n```"
        history[-1][1] += error_message
        import traceback

        traceback.print_exc()
        yield history


# ------------------------------------------------------------------
# 4. Create Gradio Interface
# ------------------------------------------------------------------
custom_css = """

#chatbot .message-bubble-content { color: #000000 !important; }

#chatbot code { background-color: #f0f0f0; border-radius: 4px; padding: 2px 4px; color: #c7254e; }

#chatbot pre { background-color: #f5f5f5; padding: 10px; border-radius: 5px; }

.footer { text-align: center; color: #777; font-size: 0.9em; padding-top: 20px; }

"""

with gr.Blocks(
    theme=gr.themes.Soft(primary_hue="teal", secondary_hue="lime"),
    css=custom_css,
    title="FeiMatrix Synapse",
) as demo:

    gr.Markdown(
        """

        # 🚀 FeiMatrix Synapse - Intelligent AI Assistant

        ---

        ### Core Concept & Philosophy

        **FeiMatrix Synapse** is a **Proof of Concept** for an advanced AI assistant.

        Its core objective is to demonstrate an AI workflow made to **autonomously understand, plan, and utilize external tools** to solve problems.



        ### The Demo Flow Explained

        When you ask a question, you can clearly observe each step of the AI's "thought process":



        1.  **🤔 Analyzing the Problem**: The AI first understands your natural language command.

        2.  **🔍 Recommending Tools**: The system searches its internal "Tool Library" using vector similarity to recommend the most relevant tools.

        3.  **🧠 AI Decision-Making**: The AI Brain (Gemini Model) makes the final choice from the recommended tools and extracts the necessary parameters from your query.

        4.  **⚙️ Executing the Tool**: The system invokes the corresponding Python function (e.g., an API call or web scraper) to fetch external, real-time information.

        5.  **✍️ Generating the Answer**: The AI synthesizes the data returned by the tool with your original question to generate a final, natural-language response.

        

        ---



        ### ⚠️ Important Disclaimer

        - **This is a technical demonstration, not a production-ready application.**

        - **All data returned by the tools (e.g., stock prices, news) is for **demonstration purposes only** using simulated or non-real-time data. Do not use it for any real-world decisions.**

        - **The primary focus of this project is to showcase the AI's "chain of thought" and "tool-using" capabilities.**

        """
    )

    chatbot = gr.Chatbot(
        [],
        elem_id="chatbot",
        label="Chat Window",
        height=650,
        avatar_images=(None, "assets/web-app-manifest-192x192.png"),
    )

    with gr.Row():
        text_input = gr.Textbox(
            scale=4,
            show_label=False,
            placeholder="Ask a question, e.g., 'What is the stock price of Apple (AAPL)?' or 'What's the latest news on AI-driven drug discovery?'",
            container=False,
        )
        submit_button = gr.Button("Send", variant="primary", scale=1, min_width=150)

    gr.Examples(
        examples=[
            "What is the stock price of Apple (AAPL)?",
            "What is the latest news about AI-driven drug discovery?",
            "Hello, what can you do?",
            "Write a quicksort algorithm in Python",
        ],
        inputs=text_input,
        label="Examples",
    )

    gr.Markdown(
        """

        ---

        <div class="footer">

        <p><strong>FeiMatrix Synapse v1.0</strong></p>

        <p>This project was conceived and directed by <strong>FeiMatrix</strong>, who also led the debugging and deployment.</p>

        <p>The system architecture, core program, and interface were co-developed with <strong>Gemini 2.5 Pro (AI)</strong>.</p>

        </div>

        """,
        elem_classes="footer",
    )

    submit_event = text_input.submit(
        fn=handle_user_message,
        inputs=[text_input, chatbot],
        outputs=[text_input, chatbot],
        queue=False,
    ).then(fn=generate_bot_response, inputs=[chatbot], outputs=[chatbot])

    submit_button.click(
        fn=handle_user_message,
        inputs=[text_input, chatbot],
        outputs=[text_input, chatbot],
        queue=False,
    ).then(fn=generate_bot_response, inputs=[chatbot], outputs=[chatbot])

# ------------------------------------------------------------------
# 5. Launch the Application
# ------------------------------------------------------------------
if __name__ == "__main__":
    demo.queue()
    demo.launch(debug=True, favicon_path="assets/favicon.ico")