import os
import gradio as gr
from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv

load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

# LLM setup
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.5, openai_api_key=openai_api_key)

# Agents
intel_agent = Agent(
    role="Intelligence Analyst",
    goal="Generate detailed intelligence based on given input.",
    backstory="Trained in data synthesis, aerial recon, signal intercepts, and pattern analysis.",
    llm=llm,
    verbose=True
)

logistics_agent = Agent(
    role="Field Logistics Coordinator",
    goal="Optimize supply chains, manage resource allocation, and coordinate unit mobility in low-infrastructure zones.",
    backstory="Expert in supply caching, route planning, and adaptive logistics under hostile conditions.",
    llm=llm,
    verbose=True
)

psyops_agent = Agent(
    role="Psychological Operations Planner",
    goal="Design and execute operations to degrade enemy morale, manipulate public perception, and sow internal division.",
    backstory="Specialist in narrative control, propaganda deployment, and cultural exploitation tactics.",
    llm=llm,
    verbose=True
)

# Process query
def process_query(user_input):
    task1 = Task(
        description=f"Analyze the following scenario for intelligence: {user_input}",
        agent=intel_agent,
        expected_output="A structured intelligence assessment."
    )

    task2 = Task(
        description=f"Plan logistics for the scenario: {user_input}",
        agent=logistics_agent,
        expected_output="A detailed logistics and resource movement plan."
    )

    task3 = Task(
        description=f"Devise psyops strategy for the scenario: {user_input}",
        agent=psyops_agent,
        expected_output="A step-by-step psyops strategy."
    )

    crew = Crew(agents=[intel_agent, logistics_agent, psyops_agent], tasks=[task1, task2, task3], verbose=False)
    results = crew.kickoff()

    intel_res = f"### 🛰 Intelligence Analyst\n{results.tasks_output[0].raw}"
    logistics_res = f"### 🚚 Field Logistics Coordinator\n{results.tasks_output[1].raw}"
    psyops_res = f"### 🧠 Psychological Operations Planner\n{results.tasks_output[2].raw}"

    return intel_res, logistics_res, psyops_res

# Gradio UI
with gr.Blocks(css="""
body {
    background: linear-gradient(135deg, #0f2027, #203a43, #2c5364);
    color: white;
}
.agent-box {
    padding: 15px;
    border-radius: 10px;
    margin: 10px;
    background: rgba(255, 255, 255, 0.08);
    box-shadow: 0px 0px 10px rgba(0,0,0,0.3);
    min-height: 200px;
}
h1 {
    text-align: center;
    color: #ffcc00;
}
""") as demo:
    gr.Markdown("# Guerrilla Warfare Agent")
    with gr.Row():
        user_input = gr.Textbox(label="Enter Intel", placeholder="Type your intel here...", lines=3)
    submit_btn = gr.Button("Run Analysis", variant="primary")

    with gr.Row():
        intel_output = gr.Markdown(elem_classes="agent-box")
        logistics_output = gr.Markdown(elem_classes="agent-box")
        psyops_output = gr.Markdown(elem_classes="agent-box")

    submit_btn.click(process_query, inputs=[user_input], outputs=[intel_output, logistics_output, psyops_output])

demo.launch()