# app.py
import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
import urllib.parse
from datetime import datetime

# -----------------------------
# 1️⃣ RFT Equation0 Computation
# -----------------------------
def compute_E0(phi, tau_eff, grad_phi, dT, gvu):
    """
    Computes Equation0
    """
    D_render = grad_phi / (1 + tau_eff)
    E0 = (D_render / dT) * (1 / gvu)
    return round(D_render, 5), round(E0, 6)

# -----------------------------
# 2️⃣ Heatmap Generation
# -----------------------------
def generate_heatmap(E0, phi):
    """
    Generates a simple heatmap image from E0 values
    """
    data = np.outer(phi, phi) * E0  # simple demo, can adjust to real field
    plt.figure(figsize=(5,5))
    plt.imshow(data, cmap='hot', interpolation='nearest')
    plt.colorbar(label="E0 intensity")
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"heatmap_{timestamp}.png"
    plt.savefig(filename, bbox_inches='tight')
    plt.close()
    return filename

# -----------------------------
# 3️⃣ Viral Twitter/X Link
# -----------------------------
def twitter_share_link(caption, image_url=None):
    """
    Returns a clickable Twitter/X intent URL
    """
    base_url = "https://twitter.com/intent/tweet?"
    params = {"text": caption}
    if image_url:
        params["url"] = image_url
    query_string = urllib.parse.urlencode(params)
    return f"<a href='{base_url}{query_string}' target='_blank'>Click to Tweet 📢</a>"

def generate_caption(E0, risk_level):
    """
    Generates a viral-ready caption
    """
    return f"RFT Prediction Alert 🚨\nE0={E0}, Risk={risk_level}\nCheck full RFT analysis! #RFTsystems #Equation0"

# -----------------------------
# 4️⃣ Risk Assessment
# -----------------------------
def assess_risk(E0):
    if E0 < 0.001:
        return "Stable ✅"
    elif 0.001 <= E0 < 0.01:
        return "Mild Stress ⚠️"
    elif 0.01 <= E0 < 0.1:
        return "Pre-Seismic ⚠️🚨"
    else:
        return "Imminent Collapse ⚡🚨"

# -----------------------------
# 5️⃣ Full Pipeline
# -----------------------------
def full_pipeline(phi, tau_eff, grad_phi, dT, gvu):
    D_render, E0 = compute_E0(phi, tau_eff, grad_phi, dT, gvu)
    risk = assess_risk(E0)
    heatmap_file = generate_heatmap(E0, np.linspace(0, phi, 10))
    caption = generate_caption(E0, risk)
    tweet_link = twitter_share_link(caption, image_url=None)  # Optional: host heatmap online to attach
    return D_render, E0, risk, heatmap_file, tweet_link

# -----------------------------
# 6️⃣ Gradio Interface
# -----------------------------
with gr.Blocks() as demo:
    gr.Markdown("# ⚡ RFT Equation0 Prediction System")
    
    with gr.Row():
        phi_input = gr.Number(label="Φ (Awareness Field)", value=0.5)
        tau_input = gr.Number(label="τ_eff (Collapse Torque)", value=1.72)
        grad_phi_input = gr.Number(label="∇Φ (Field Gradient)", value=0.88)
        dT_input = gr.Number(label="∇Tₚ (Temporal Pressure)", value=2.61)
        gvu_input = gr.Number(label="GVU (Grinstead Voyager Unit)", value=242.718)
    
    compute_btn = gr.Button("Compute Prediction ⚡")
    
    with gr.Row():
        D_render_out = gr.Textbox(label="D_render")
        E0_out = gr.Textbox(label="E0")
        risk_out = gr.Textbox(label="Risk Level")
    
    heatmap_out = gr.Image(label="Heatmap")
    tweet_link_out = gr.HTML(label="Share to Twitter/X")

    compute_btn.click(
        fn=full_pipeline,
        inputs=[phi_input, tau_input, grad_phi_input, dT_input, gvu_input],
        outputs=[D_render_out, E0_out, risk_out, heatmap_out, tweet_link_out]
    )

demo.launch()