added UI demo

main.py

@@ -1,6 +1,248 @@
-def main():
-    print("Hello from ai-peer-conversation!")
+import time
+import openai
+import random
+import gradio as gr
+from datetime import datetime
 
 
+OPENAI_API_KEY = "ollama"
+OPENAI_API_BASE = "http://localhost:11434"
+MODEL_NAME = "llama3.1:8b-instruct-q2_K"
+
+llm_client = openai.AsyncClient(
+    api_key=OPENAI_API_KEY,
+    base_url=OPENAI_API_BASE,
+)
+
+
+# Mock AI response function (replace with your actual AI integration)
+def generate_ai_response(message, system_message, agent_name):
+    """
+    Mock function to simulate AI responses.
+    Replace this with your actual AI API calls (OpenAI, Anthropic, etc.)
+    """
+    # Simulate thinking time
+    time.sleep(0.1)
+
+    # Mock responses based on agent
+    if agent_name == "Agent A":
+        responses = [
+            f"As Agent A, I think {message.lower()} raises interesting points about efficiency.",
+            f"From my perspective as Agent A, I'd like to explore {message.lower()} further.",
+        ]
+    else:
+        responses = [
+            f"Agent B perspective: {message.lower()} presents some challenges we should address.",
+            f"As Agent B, I have a different view on {message.lower()} - let me elaborate.",
+        ]
+
+    return random.choice(responses)
+
+
+def start_conversation(system_msg_a, system_msg_b, num_turns=5):
+    """
+    Orchestrates the conversation between two AI agents
+    """
+    if not system_msg_a.strip() or not system_msg_b.strip():
+        return [
+            (
+                "System",
+                "⚠️ Please provide system messages for both agents before starting the conversation.",
+            )
+        ]
+
+    conversation = []
+    conversation.append(
+        (
+            "System",
+            f"🤖 **Conversation Started**\n\n**Agent A System Message:** {system_msg_a}\n\n**Agent B System Message:** {system_msg_b}\n\n---",
+        )
+    )
+
+    # Initial message from Agent A
+    current_message = (
+        "Hello, I'd like to start our discussion based on my system instructions."
+    )
+
+    for turn in range(num_turns):
+        # Agent A speaks
+        if turn == 0:
+            agent_a_response = generate_ai_response(
+                "starting our conversation", system_msg_a, "Agent A"
+            )
+        else:
+            agent_a_response = generate_ai_response(
+                current_message, system_msg_a, "Agent A"
+            )
+
+        conversation.append(("Agent A", agent_a_response))
+        yield conversation
+        time.sleep(0.5)  # Brief pause for visual effect
+
+        # Agent B responds
+        agent_b_response = generate_ai_response(
+            agent_a_response, system_msg_b, "Agent B"
+        )
+        conversation.append(("Agent B", agent_b_response))
+        current_message = agent_b_response
+        yield conversation
+        time.sleep(0.5)  # Brief pause for visual effect
+
+    conversation.append(("System", "🏁 **Conversation Complete**"))
+    yield conversation
+
+
+def format_conversation_for_display(conversation):
+    """
+    Formats the conversation for the chatbot display
+    """
+    formatted = []
+    for speaker, message in conversation:
+        if speaker == "System":
+            formatted.append((None, message))
+        elif speaker == "Agent A":
+            formatted.append((message, None))
+        else:  # Agent B
+            formatted.append((None, message))
+    return formatted
+
+
+def save_conversation(conversation):
+    """
+    Saves the conversation to a text file
+    """
+    if not conversation:
+        return None, "No conversation to save."
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"ai_conversation_{timestamp}.txt"
+
+    content = "AI-to-AI Conversation Log\n"
+    content += f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n"
+    content += "=" * 50 + "\n\n"
+
+    for speaker, message in conversation:
+        if speaker == "System":
+            content += f"[SYSTEM] {message}\n\n"
+        else:
+            content += f"{speaker}: {message}\n\n"
+
+    # Save to file
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(content)
+
+    return filename, f"Conversation saved as {filename}"
+
+
+# Custom CSS for better visual appeal
+with open("static/styles.css") as css_file:
+    custom_css = css_file.read()
+
+with open("static/index.html") as html_file:
+    custom_html = html_file.read()
+
+
+# Create the Gradio interface
+with gr.Blocks(css=custom_css, title="AI-to-AI Conversation Interface") as demo:
+    gr.HTML(custom_html)
+
+    # Store conversation data
+    conversation_data = gr.State([])
+
+    with gr.Tabs() as tabs:
+        with gr.TabItem("🚀 Setup & Start Conversation", id=0) as setup_tab:
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.HTML(
+                        "<h3 style='color: #667eea; margin-bottom: 15px;'>🤖 Agent A System Message</h3>"
+                    )
+                    system_msg_a = gr.Textbox(
+                        placeholder="Enter the system message for Agent A (e.g., 'You are a helpful assistant focused on creative solutions...')",
+                        lines=4,
+                        label="",
+                        elem_classes=["system-input"],
+                    )
+
+                with gr.Column(scale=1):
+                    gr.HTML(
+                        "<h3 style='color: #764ba2; margin-bottom: 15px;'>🤖 Agent B System Message</h3>"
+                    )
+                    system_msg_b = gr.Textbox(
+                        placeholder="Enter the system message for Agent B (e.g., 'You are an analytical assistant focused on logical reasoning...')",
+                        lines=4,
+                        label="",
+                        elem_classes=["system-input"],
+                    )
+
+            with gr.Row():
+                with gr.Column(scale=1):
+                    num_turns = gr.Slider(
+                        minimum=1,
+                        maximum=10,
+                        value=5,
+                        step=1,
+                        label="Number of conversation turns",
+                        info="How many back-and-forth exchanges between agents",
+                    )
+
+            with gr.Row():
+                start_btn = gr.Button(
+                    "🚀 Start AI-to-AI Conversation",
+                    size="lg",
+                    elem_classes=["start-button"],
+                )
+
+        with gr.TabItem("💬 Live Conversation", id=1) as conversation_tab:
+            gr.HTML(
+                "<h3 style='text-align: center; color: #444; margin-bottom: 20px;'>Watch the AI agents converse in real-time</h3>"
+            )
+
+            chatbot = gr.Chatbot(
+                label="AI-to-AI Conversation",
+                height=600,
+                elem_classes=["conversation-box"],
+                avatar_images=("🤖", "🔷"),
+            )
+
+            with gr.Row():
+                save_btn = gr.Button(
+                    "💾 Save Conversation", elem_classes=["save-button"]
+                )
+                download_file = gr.File(label="Download Conversation", visible=False)
+
+            status_msg = gr.Textbox(label="Status", interactive=False)
+
+    # Event handlers
+    def run_conversation(sys_a, sys_b, turns):
+        for conv in start_conversation(sys_a, sys_b, turns):
+            formatted = format_conversation_for_display(conv)
+            yield formatted, conv
+
+    def start_and_switch_tab(sys_a, sys_b, turns):
+        """Start conversation and switch to conversation tab"""
+        return gr.Tabs(selected=1), [], "🚀 Starting conversation..."
+
+    start_btn.click(
+        fn=start_and_switch_tab,
+        inputs=[system_msg_a, system_msg_b, num_turns],
+        outputs=[tabs, conversation_data, status_msg],
+    ).then(
+        fn=run_conversation,
+        inputs=[system_msg_a, system_msg_b, num_turns],
+        outputs=[chatbot, conversation_data],
+    )
+
+    def handle_save(conversation):
+        filename, message = save_conversation(conversation)
+        if filename:
+            return gr.update(visible=True, value=filename), message
+        else:
+            return gr.update(visible=False), message
+
+    save_btn.click(
+        fn=handle_save, inputs=[conversation_data], outputs=[download_file, status_msg]
+    )
+
+# Launch the interface
 if __name__ == "__main__":
-    main()
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)