app.py

import gradio as gr
from transformers import pipeline
import matplotlib.pyplot as plt
import numpy as np
import datetime
import os

# --- 1. SETUP LOCAL AI (CPU Optimized) ---
print("⏳ Initializing BioVigilus Core... please wait.")
# Using LaMini for concise, professional summaries
ai_pipe = pipeline("text2text-generation", model="MBZUAI/LaMini-Flan-T5-783M", device=-1)

# --- 2. COMPLIANCE & LOGIC ENGINE ---
def calculate_carbon_footprint(activity, disturbance):
    base_emission = 50
    if "mining" in activity.lower() or "construction" in activity.lower():
        base_emission = 500
    loss_factor = disturbance * 2.5 
    return int(base_emission + loss_factor)

def calculate_economic_loss(habitat, region, disturbance):
    habitat_value = 1500 
    if "forest" in habitat.lower() or "cliffs" in habitat.lower():
        habitat_value = 5000
    loss = habitat_value * disturbance * 10 
    return f"${loss:,.2f}"

def check_compliance(noise, flow, disturbance):
    violations = []
    if noise > 85: violations.append("❌ **Noise Violation:** Exceeds 85dB limit.")
    if flow > 50: violations.append("❌ **Aquatic Violation:** Flow diversion > 50%.")
    if disturbance > 70: violations.append("❌ **Land Violation:** Critical habitat loss > 70%.")
    if not violations: return "✅ **Compliant:** Within standards."
    return "<br>".join(violations)

def get_mitigation_plan(noise, flow, disturbance, breeding):
    strategies = []
    if noise > 60:
        strategies.append("🔊 **Acoustic Buffering:** Install 4m noise barriers.")
        strategies.append("⏰ **Curfew:** No machinery 18:00-06:00.")
    if disturbance > 40:
        strategies.append("🌱 **Bio-Restoration:** 2:1 Replanting commitment.")
        strategies.append("🚧 **Buffer Zones:** 500m exclusion zone.")
    if flow > 30:
        strategies.append("💧 **Aquatic Bypass:** Install culverts.")
    if breeding == "Yes":
        strategies.append("🛑 **SEASONAL STOP-WORK:** Mandatory shutdown.")
    if not strategies:
        strategies.append("✅ Standard Monitoring.")
    return strategies

# --- 3. VISUALIZATION ENGINE ---
def create_dashboard_plots(noise, flow, disturbance, breeding):
    fig = plt.figure(figsize=(12, 5))
    fig.patch.set_facecolor('#0f172a')

    # Radar Chart
    ax1 = fig.add_subplot(131, polar=True)
    labels = ['Noise', 'Water', 'Land']
    stats = [noise, flow, disturbance]
    stats_loop = np.concatenate((stats, [stats[0]]))
    angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()
    angles += angles[:1]
    ax1.fill(angles, stats_loop, color='#10b981', alpha=0.4)
    ax1.plot(angles, stats_loop, color='#34d399', linewidth=2)
    ax1.set_facecolor('#0f172a')
    ax1.set_xticks(angles[:-1])
    ax1.set_xticklabels(labels, color='white', size=9, weight='bold')
    ax1.tick_params(axis='y', colors='#94a3b8')
    ax1.set_title("Threat Profile", color='white', pad=15)

    # Seasonal Chart
    ax2 = fig.add_subplot(132)
    ax2.set_facecolor('#0f172a')
    months = ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']
    base_risk = [noise/2 + disturbance/3] * 12
    if breeding == "Yes":
        for i in range(3, 8): base_risk[i] += 30
    ax2.plot(months, base_risk, color='#facc15', marker='o')
    ax2.set_ylim(0, 100)
    ax2.tick_params(colors='white')
    ax2.set_title("Seasonal Risk", color='white', pad=15)

    # Compliance Chart
    ax3 = fig.add_subplot(133)
    ax3.set_facecolor('#0f172a')
    metrics = ['Comp.', 'Risk']
    scores = [100 - (len(check_compliance(noise, flow, disturbance))/5 * 20), (noise+flow+disturbance)/3]
    ax3.bar(metrics, scores, color=['#3b82f6', '#ef4444'], alpha=0.7)
    ax3.set_ylim(0, 100)
    ax3.tick_params(colors='white')
    ax3.set_title("Scorecard", color='white', pad=15)

    plt.tight_layout()
    return fig

# --- 4. MAIN REPORT GENERATOR ---
def generate_full_report(species, habitat, region, activity, noise, flow, pollutants, breeding, disturbance):
    
    risk_score = int((noise + flow + disturbance) / 3)
    if breeding == "Yes": risk_score += 15
    risk_score = min(risk_score, 100)
    
    risk_level = "LOW"
    if risk_score > 75: risk_level = "CRITICAL"
    elif risk_score > 50: risk_level = "HIGH"
    elif risk_score > 25: risk_level = "MODERATE"

    carbon = calculate_carbon_footprint(activity, disturbance)
    money_loss = calculate_economic_loss(habitat, region, disturbance)
    compliance = check_compliance(noise, flow, disturbance)
    mitigation = get_mitigation_plan(noise, flow, disturbance, breeding)
    
    prompt = f"""
    Write an executive summary for an EIA.
    Project: {activity} on {species} in {region}.
    Threats: Noise {noise}%, Habitat Loss {disturbance}%.
    """
    try:
        ai_sum = ai_pipe(prompt, max_length=200, do_sample=True, temperature=0.6)[0]['generated_text']
    except:
        ai_sum = "Executive summary generation unavailable."

    timestamp = datetime.datetime.now().strftime("%Y-%m-%d")
    mitigation_html = "".join([f"<li>{m}</li>" for m in mitigation])
    
    html_report = f"""
    <div style="font-family: sans-serif; color: #333;">
        <h1 style="color: #2c3e50;">🌿 BioVigilus Impact Report</h1>
        <p><strong>Date:</strong> {timestamp} | <strong>Analyst:</strong> Abdullah</p>
        <div style="background-color: #f8f9fa; padding: 15px; border-left: 5px solid #10b981;">
            <h3>1. Executive Summary</h3>
            <p>{ai_sum}</p>
        </div>
        <h3>2. Economic & Carbon Data</h3>
        <p><strong>Carbon Footprint:</strong> {carbon} tons/yr <br> <strong>Ecosystem Loss:</strong> {money_loss}/yr</p>
        <h3>3. Mitigation Plan</h3>
        <ul>{mitigation_html}</ul>
    </div>
    """
    
    filename = "BioVigilus_Official_Report.html"
    with open(filename, "w") as f:
        f.write(html_report)

    display_text = f"""
    ### 📋 Executive Summary
    {ai_sum}

    ---
    ### 🌍 Carbon & Economic Impact
    * **Est. Carbon Footprint:** {carbon} tons CO2e / year
    * **Ecosystem Value Loss:** {money_loss} / year
    
    ### ⚖️ Regulatory Compliance
    {compliance}

    ### 🛡️ Mitigation Strategy
    {chr(10).join(['- ' + m for m in mitigation])}
    
    **🚨 BioVigilus Risk Score:** {risk_score}/100 ({risk_level})
    """

    return display_text, create_dashboard_plots(noise, flow, disturbance, breeding), filename

# --- 5. UI LAYOUT ---
custom_css = """
<style>
    body, .gradio-container { background-color: #0f172a !important; color: #e2e8f0 !important; }
    .input-panel { background-color: #1e293b; padding: 20px; border-radius: 12px; border: 1px solid #334155; }
    button.primary { background: linear-gradient(135deg, #059669, #10b981) !important; color: white; font-weight: bold; }
    .output-text { font-family: sans-serif; background-color: #1e293b; padding: 20px; border-radius: 12px; border: 1px solid #334155; font-size: 15px; line-height: 1.6; }
    h1, h3 { text-align: center; color: #34d399; }
</style>
"""

with gr.Blocks() as app:
    gr.HTML(custom_css)
    
    with gr.Row():
        gr.Markdown("# 🌿 BioVigilus Pro")
    gr.Markdown("### *Advanced Environmental Intelligence | Deployed by Abdullah*")

    with gr.Row():
        with gr.Column(scale=1, elem_classes=["input-panel"]):
            gr.Markdown("### 📝 Project Parameters")
            species = gr.Textbox(label="Species", value="Markhor")
            habitat = gr.Textbox(label="Habitat", value="Cliffs")
            region = gr.Textbox(label="Region", value="Chitral")
            activity = gr.Textbox(label="Activity", value="Mining")
            breeding = gr.Radio(["Yes", "No"], label="Breeding Season?", value="Yes")
            
            gr.Markdown("### 📊 Environmental Metrics")
            noise = gr.Slider(0, 100, label="Noise Level", value=88)
            flow = gr.Slider(0, 100, label="Water Flow Impact", value=25)
            disturbance = gr.Slider(0, 100, label="Land Disturbance", value=75)
            pollutants = gr.Textbox(label="Pollutants", value="Heavy Metals")
            
            btn = gr.Button("🚀 GENERATE COMPREHENSIVE REPORT", variant="primary")

        with gr.Column(scale=2):
            with gr.Tabs():
                with gr.TabItem("📄 Analysis"):
                    output_text = gr.Markdown("Ready for analysis...", elem_classes=["output-text"])
                with gr.TabItem("📈 Multi-View Charts"):
                    output_plot = gr.Plot(label="Impact Metrics")
                with gr.TabItem("📥 Export"):
                    gr.Markdown("### Download Official Report")
                    download_btn = gr.File(label="Download HTML Report")

    btn.click(
        generate_full_report,
        inputs=[species, habitat, region, activity, noise, flow, pollutants, breeding, disturbance],
        outputs=[output_text, output_plot, download_btn]
    )

if __name__ == "__main__":
    app.launch(server_name="0.0.0.0", server_port=7860, share=False, ssr_mode=False)