Update app.py

app.py

@@ -1,81 +1,109 @@
+# app.py
 import gradio as gr
 import numpy as np
 import matplotlib.pyplot as plt
+import urllib.parse
+from datetime import datetime
 
 # -----------------------------
-# Core RFT Computation Function
+# 1️⃣ RFT Equation0 Computation
 # -----------------------------
-def compute_rft(phi, tau_eff, grad_phi, delta_t):
-    # Render density
+def compute_E0(phi, tau_eff, grad_phi, dT, gvu):
+    """
+    Computes Equation0
+    """
     D_render = grad_phi / (1 + tau_eff)
-    
-    # Temporal-pressure gradient (simulated evolution)
-    T_current = phi * tau_eff
-    T_next = (phi + delta_t*0.05) * (tau_eff + delta_t*0.05)  # simple evolution
-    grad_T = (T_next - T_current) / delta_t
-    
-    # Equation0 (normalized by GVU)
-    GVU = 242.718
-    E0 = (D_render / grad_T) / GVU
-    
-    # Risk level indicator
+    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:
-        risk = "✅ Stable"
-        color = "green"
-    elif E0 < 0.01:
-        risk = "⚠️ Mild Stress"
-        color = "yellow"
-    elif E0 < 0.1:
-        risk = "🔥 Pre-Seismic"
-        color = "orange"
+        return "Stable ✅"
+    elif 0.001 <= E0 < 0.01:
+        return "Mild Stress ⚠️"
+    elif 0.01 <= E0 < 0.1:
+        return "Pre-Seismic ⚠️🚨"
     else:
-        risk = "🚨 Imminent Collapse"
-        color = "red"
-    
-    # Generate heatmap
-    size = 50
-    heatmap = np.array([[D_render*100 + i*3 + j*2 for i in range(size)] for j in range(size)])
-    
-    fig, ax = plt.subplots(figsize=(4,4))
-    cax = ax.imshow(heatmap, cmap="plasma")
-    ax.set_title("Collapse Render Heatmap")
-    ax.axis("off")
-    fig.colorbar(cax, ax=ax, fraction=0.046, pad=0.04)
-    
-    return D_render, E0, risk, fig
+        return "Imminent Collapse ⚡🚨"
 
 # -----------------------------
-# Gradio Interface
+# 5️⃣ Full Pipeline
 # -----------------------------
-with gr.Blocks(title="RFTSystems — Hidden Universe Explorer") as demo:
-    gr.Markdown(
-        """
-        # 🌌 RFTSystems Interactive Universe
-        Adjust the awareness field, collapse torque, and gradients. 
-        Watch the universe respond. 
-        Share your results! 
-        """
-    )
-    
-    with gr.Row():
-        phi_slider = gr.Slider(label="Awareness Field Φ", minimum=0, maximum=1, step=0.01, value=0.5)
-        tau_slider = gr.Slider(label="Collapse Torque τ_eff", minimum=0, maximum=3, step=0.01, value=1.5)
-    with gr.Row():
-        grad_slider = gr.Slider(label="Field Gradient ∇Φ", minimum=0, maximum=2, step=0.01, value=0.5)
-        dt_slider = gr.Slider(label="Time Step Δt", minimum=0.01, maximum=1, step=0.01, value=0.1)
+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():
-        d_render_out = gr.Number(label="Render Density D_render")
-        e0_out = gr.Number(label="Equation0 E₀")
-        risk_out = gr.Label(label="Seismic Risk Level")
+        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)
     
-    heatmap_out = gr.Plot(label="Collapse Heatmap")
+    compute_btn = gr.Button("Compute Prediction ⚡")
     
-    # Connect sliders to compute function
-    phi_slider.change(compute_rft, inputs=[phi_slider, tau_slider, grad_slider, dt_slider], outputs=[d_render_out, e0_out, risk_out, heatmap_out])
-    tau_slider.change(compute_rft, inputs=[phi_slider, tau_slider, grad_slider, dt_slider], outputs=[d_render_out, e0_out, risk_out, heatmap_out])
-    grad_slider.change(compute_rft, inputs=[phi_slider, tau_slider, grad_slider, dt_slider], outputs=[d_render_out, e0_out, risk_out, heatmap_out])
-    dt_slider.change(compute_rft, inputs=[phi_slider, tau_slider, grad_slider, dt_slider], outputs=[d_render_out, e0_out, risk_out, heatmap_out])
+    with gr.Row():
+        D_render_out = gr.Textbox(label="D_render")
+        E0_out = gr.Textbox(label="E0")
+        risk_out = gr.Textbox(label="Risk Level")
     
-# Launch app
+    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()