import datetime
import logging
import sys
import uuid
from pathlib import Path
import csv
import pandas as pd
import gradio as gr
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from simple_salesforce import Salesforce
import base64
import os
import locale
import numpy as np

try:
    locale.setlocale(locale.LC_ALL, 'en_IN.UTF-8')
except locale.Error:
    locale.setlocale(locale.LC_ALL, '')

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[logging.FileHandler('app_log.txt'), logging.StreamHandler(sys.stdout)]
)
logger = logging.getLogger(__name__)

SALESFORCE_USERNAME = "vaneshdevarapalli866@agentforce.com"
SALESFORCE_PASSWORD = "vanesh@331"
SALESFORCE_SECURITY_TOKEN = "VRUVbBOdG0s9Q4xy0W6DB1Y6b"

def connect_to_salesforce():
    try:
        sf_instance = Salesforce(
            username=SALESFORCE_USERNAME,
            password=SALESFORCE_PASSWORD,
            security_token=SALESFORCE_SECURITY_TOKEN,
        )
        logger.info("Connected to Salesforce successfully.")
        return sf_instance
    except Exception as e:
        logger.error(f"Salesforce connection failed: {e}")
        raise

try:
    sf = connect_to_salesforce()
except Exception as e:
    logger.error(f"Failed to initialize Salesforce connection: {e}")
    sf = None

equipment_choices = [
    "Bulldozer", "Crane", "Excavator", "Loader", "Forklift",
    "Backhoe", "Grader", "Scraper", "Dump Truck", "Roller"
]
project_choices = [
    "Project Alpha", "Project Beta", "Project Gamma", "Project Delta", "Project Epsilon",
    "Project Zeta", "Project Theta", "Project Sigma", "Project Omega", "Project Phoenix"
]
ai_suggestion_choices = ["Move", "Repair", "Replace"]  # Removed "Pause Rent"

def validate_date(last_maint):
    try:
        # Check if the provided date is in the future
        if last_maint and last_maint != "N/A":
            entered_date = datetime.datetime.strptime(last_maint, "%Y-%m-%d")
            current_date = datetime.datetime.today()

            if entered_date > current_date:
                raise ValueError(f"Last Maintenance Date cannot be in the future. You entered: {last_maint}")
        return last_maint
    except ValueError as ve:
        raise ValueError(f"Invalid date format for Last Maintenance. Please use YYYY-MM-DD. Error: {ve}")

def call_ai_model(usage, idle, freq, cost, last):
    try:
        usage = float(usage) if usage is not None and not np.isnan(usage) else 0.0
        idle = float(idle) if idle is not None and not np.isnan(idle) else 0.0
        freq = float(freq) if freq is not None and not np.isnan(freq) else 0.0
        cost = float(cost) if cost is not None and not np.isnan(cost) else 0.0

        total = usage + idle
        utilization_ratio = usage / total if total > 0 else 0.0
        utilization_percent = utilization_ratio * 100

        # Determine suggestion (no "Pause Rent")
        if utilization_percent < 60:
            sug = "Move"
        elif utilization_percent < 80:
            sug = "Repair"
        else:
            sug = "Replace"

        base_conf = utilization_ratio
        if idle > usage:
            diff_ratio = (idle - usage) / total
            base_conf -= diff_ratio * 0.5

        freq_factor = min(freq / 10.0, 1.0)
        base_conf *= (0.7 + 0.3 * freq_factor)
        confidence = max(0.05, min(base_conf, 1.0))

        # Round before return
        return sug, round(confidence, 2), round(utilization_percent, 2)

    except Exception as e:
        logger.error(f"Error in call_ai_model: {e}")
        raise ValueError(f"AI model computation failed: {str(e)}")

def process_equipment_utilization(equip, proj, use_h, idle_h, move_f, cost_h, last_maint, ai_sug):
    try:
        if not sf:
            raise ValueError("Salesforce connection is not initialized. Please check credentials and try again.")

        # Validate the Last Maintenance date
        last_maint = validate_date(last_maint)

        # Always run AI model to get suggestion, confidence, score
        ai_sug_generated, conf, score = call_ai_model(use_h, idle_h, move_f, cost_h, last_maint)

        # Use manual suggestion if provided, else AI suggestion
        suggestion_to_use = ai_sug if ai_sug else ai_sug_generated

        for field, value in [("Usage Hours", use_h), ("Idle Hours", idle_h), 
                            ("Movement Frequency", move_f), ("Cost per Hour", cost_h),
                            ("Confidence", conf), ("Utilization Score", score)]:
            if value is None or np.isnan(value):
                raise ValueError(f"Invalid value for {field}: {value}. Must be a valid number.")

        if last_maint is None or pd.isna(last_maint):
            last_maint = "N/A"

        summary = {
            "Equipment Name": equip,
            "Project": proj,
            "Usage Hours": use_h,
            "Idle Hours": idle_h,
            "Suggestion": suggestion_to_use,
            "Confidence": conf * 100,    # Store as percentage (0-100)
            "Utilization Score": score,
            "Cost per Hour": cost_h,
            "Last Maintenance": last_maint or "N/A"
        }
        record_data = {
            "Equipment_Name__c": equip,
            "Project_Name__c": proj,
            "Usage_Hours__c": float(use_h),
            "Idle_Hours__c": float(idle_h),
            "AI_Suggestion__c": suggestion_to_use,
            "Suggestion_Confidence__c": float(conf * 100),
            "Utilization_Score__c": float(score),
            "Cost_per_Hour__c": float(cost_h),
            "Report_Link__c": "Pending",
            "Last_Maintenance__c": last_maint if last_maint != "N/A" else None,
            "Dashboard_Flag__c": False
        }

        for key, value in record_data.items():
            if isinstance(value, float) and np.isnan(value):
                raise ValueError(f"Field {key} contains NaN, which is not allowed in Salesforce requests.")

        logger.info(f"Sending record data to Salesforce: {record_data}")

        resp = sf.Equipment_Utilization_Record__c.create(record_data)
        if not resp.get("success"):
            raise ValueError(f"Failed to create Salesforce record: {resp}")
        rec_id = resp.get("id")
        if not rec_id:
            raise ValueError("Salesforce record creation succeeded, but no record ID was returned.")

        safe_equip = equip.replace(" ", "_")
        safe_proj = proj.replace(" ", "_")
        uid = uuid.uuid4().hex[:8]
        pdf_path = Path(f"static/reports/report_{safe_equip}_{safe_proj}_{uid}.pdf")
        pdf_path.parent.mkdir(parents=True, exist_ok=True)

        c = canvas.Canvas(str(pdf_path), pagesize=letter)
        c.setFont("Helvetica-Bold", 14)
        title_str = f"Equipment Utilization Report - {equip} ({proj})"
        c.drawString(100, 750, title_str)

        c.setFont("Helvetica", 12)
        c.drawString(100, 730, f"Record ID: {rec_id}")
        y = 710
        for k, v in summary.items():
            c.drawString(100, y, f"{k}: {v}")
            y -= 20
        c.save()

        encoded = base64.b64encode(pdf_path.read_bytes()).decode()
        cv = sf.ContentVersion.create({
            "Title": "UtilReport",
            "PathOnClient": os.path.basename(str(pdf_path)),
            "VersionData": encoded,
            "FirstPublishLocationId": rec_id
        })
        if not cv.get("success"):
            raise ValueError(f"Failed to upload PDF to Salesforce: {cv}")
        pdf_url = f"https://{sf.sf_instance}/sfc/servlet.shepherd/version/download/{cv['id']}"
        sf.Equipment_Utilization_Record__c.update(rec_id, {"Report_Link__c": pdf_url})

        return {
            "Salesforce_Record_Id": rec_id,
            "Summary": summary,
            "Report_Link": pdf_url,
            "Report_File_Path": str(pdf_path)
        }
    except Exception as e:
        logger.error(f"Error in process_equipment_utilization: {e}")
        raise ValueError(f"Processing failed: {str(e)}")

def format_output(result):
    summary = result.get("Summary", {})
    cost_val = summary.get("Cost per Hour", 0)
    try:
        cost_str = locale.currency(cost_val, grouping=True)
    except:
        cost_str = f"₹{cost_val:,.2f}"
    conf_pct = summary.get("Confidence", 0)  # Already percentage (0-100)
    util_score = summary.get("Utilization Score", 0)  # Already percentage (0-100)
    lines = [
        f" • AI Suggestion: {summary.get('Suggestion', 'N/A')}",
        f" • Suggestion Confidence: {conf_pct:.2f}%",
        f" • Utilization Score: {util_score:.2f}%",
        f" • Equipment Name: {summary.get('Equipment Name', 'N/A')}",
        f" • Project: {summary.get('Project', 'N/A')}",
        f" • Usage Hours: {summary.get('Usage Hours', 0):.2f}",
        f" • Idle Hours: {summary.get('Idle Hours', 0):.2f}",
        f" • Cost per Hour: {cost_str}",
        f" • Last Maintenance: {summary.get('Last Maintenance', 'N/A')}"
    ]
    return "\n".join(lines)

def format_batch_output(records):
    lines = []
    for i, rec in enumerate(records, 1):
        summary = rec.get("Summary", {})
        
        # Detailed record output format
        record_details = [
            f"Record {i}:",
            f" • Record ID: {rec['Salesforce_Record_Id']}",
            f" • Equipment Name: {summary.get('Equipment Name', 'N/A')}",
            f" • Project: {summary.get('Project', 'N/A')}",
            f" • Usage Hours: {summary.get('Usage Hours', 0):.2f}",
            f" • Idle Hours: {summary.get('Idle Hours', 0):.2f}",
            f" • Suggestion: {summary.get('Suggestion', 'N/A')}",
            f" • Suggestion Confidence: {summary.get('Confidence', 0):.2f}%",
            f" • Utilization Score: {summary.get('Utilization Score', 0):.2f}%",
            f" • Cost per Hour: ₹{summary.get('Cost per Hour', 0):,.2f}",
            f" • Last Maintenance: {summary.get('Last Maintenance', 'N/A')}"
        ]
        lines.append("\n".join(record_details))
        lines.append("\n---\n")  # Separator between records
    return "\n".join(lines)

def generate_batch_pdf(records, batch_uid):
    pdf_path = Path(f"static/reports/batch_report_{batch_uid}.pdf")
    pdf_path.parent.mkdir(parents=True, exist_ok=True)

    c = canvas.Canvas(str(pdf_path), pagesize=letter)
    c.setFont("Helvetica-Bold", 14)
    y = 760

    for idx, record in enumerate(records, 1):
        if y < 100:
            c.showPage()
            c.setFont("Helvetica-Bold", 14)
            c.drawString(100, 770, "Equipment Utilization Batch Report")
            c.setFont("Helvetica", 12)
            y = 750

        if y == 760:
            c.drawString(100, y, "Equipment Utilization Batch Report")
            y -= 20
            c.setFont("Helvetica", 12)

        c.drawString(100, y, f"Record {idx}: {record['Summary']['Equipment Name']}")
        y -= 20
        c.drawString(100, y, f"Salesforce Record ID: {record['Salesforce_Record_Id']}")
        y -= 20

        for key, value in record["Summary"].items():
            c.drawString(100, y, f"{key}: {value}")
            y -= 20
            if y < 100:
                c.showPage()
                c.setFont("Helvetica-Bold", 14)
                c.drawString(100, 770, "Equipment Utilization Batch Report")
                c.setFont("Helvetica", 12)
                y = 750

        y -= 10
        c.drawString(100, y, "-" * 50)
        y -= 20

    c.save()
    return str(pdf_path)

def manual_input(equipment, project, usage, idle, freq, cost, last, ai_suggestion):
    try:
        if not equipment or equipment not in equipment_choices:
            raise ValueError("Please select a valid Equipment Name.")
        if not project or project not in project_choices:
            raise ValueError("Please select a valid Project Name.")
        if usage is None or usage < 0 or np.isnan(usage):
            raise ValueError("Usage Hours must be a non-negative number.")
        if idle is None or idle < 0 or np.isnan(idle):
            raise ValueError("Idle Hours must be a non-negative number.")
        if freq is None or freq < 0 or np.isnan(freq):
            raise ValueError("Movement Frequency must be a non-negative number.")
        if cost is None or cost < 0 or np.isnan(cost):
            raise ValueError("Cost per Hour must be a non-negative number.")

        last_val = last or "N/A"
        res = process_equipment_utilization(equipment, project, usage, idle, freq, cost, last_val, ai_suggestion)
        formatted = format_output(res)
        return formatted, res.get("Report_File_Path")  # Only PDF output here now
    except Exception as e:
        logger.error(f"Error in manual_input: {e}")
        return f"Error: {str(e)}", None

def batch_upload(csv_file):
    try:
        if csv_file is None:
            return "", None

        df = pd.read_csv(csv_file.name)
        
        required_columns = ['equipment_name', 'project_name', 'usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']
        missing_columns = [col for col in required_columns if col not in df.columns]
        if missing_columns:
            raise ValueError(f"CSV file is missing required columns: {', '.join(missing_columns)}")

        NUMERIC_FIELDS = ['usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']
        for col in NUMERIC_FIELDS:
            if df[col].isna().any():
                raise ValueError(f"Column '{col}' contains missing or invalid values (e.g., NaN). Please ensure all values are valid numbers.")
            try:
                df[col] = df[col].astype(float)
            except ValueError as e:
                raise ValueError(f"Column '{col}' contains non-numeric values: {str(e)}")

        MACHINERY_FIELDS = ['equipment_name', 'project_name']
        for col in MACHINERY_FIELDS:
            if col not in df.columns:
                raise ValueError(f"Missing required column: {col}")
            if df[col].isna().any():
                raise ValueError(f"Column '{col}' contains missing values. Please ensure all rows have valid equipment and project names.")

        if 'last_maintenance' in df.columns:
            df['last_maintenance'] = df['last_maintenance'].apply(lambda x: "N/A" if pd.isna(x) else str(x))

        if 'ai_suggestion' in df.columns:
            df['ai_suggestion'] = df['ai_suggestion'].fillna('')  
            df['ai_suggestion'] = df['ai_suggestion'].apply(
                lambda x: x if x in ai_suggestion_choices else ''
            )
        else:
            df['ai_suggestion'] = ''

        records = []
        for idx, row in df.iterrows():
            try:
                last_maint = row.get('last_maintenance', 'N/A')
                if pd.isna(last_maint):
                    last_maint = "N/A"
                rec = process_equipment_utilization(
                    row['equipment_name'], row['project_name'],
                    row['usage_hours'], row['idle_hours'],
                    row['movement_frequency'], row['cost_per_hour'],
                    last_maint, row.get('ai_suggestion', '')
                )
                records.append(rec)
            except Exception as e:
                logger.error(f"Error processing row {idx + 2}: {e}")
                raise ValueError(f"Error processing row {idx + 2}: {str(e)}")

        batch_uid = uuid.uuid4().hex[:8]
        batch_pdf_path = generate_batch_pdf(records, batch_uid)
        formatted_text = format_batch_output(records)

        return formatted_text, batch_pdf_path
    except Exception as e:
        logger.error(f"Error in batch_upload: {e}")
        return f"Error: {str(e)}", None

with gr.Blocks() as app:
    gr.Markdown("## 📋 Equipment Utilization Record Uploader", elem_id="app-title")
    with gr.Tabs():
        with gr.TabItem("Manual Input"):
            with gr.Group():
                equipment_dropdown = gr.Dropdown(equipment_choices, label="🔧 Equipment Name")
                project_dropdown   = gr.Dropdown(project_choices,  label="🏍️ Project Name")
                ai_dropdown        = gr.Dropdown([""] + ai_suggestion_choices, label="🧐 AI Suggestion")
            with gr.Group():
                with gr.Row():
                    usage = gr.Number(label="⏱️ Usage Hours", value=0, minimum=0)
                    idle  = gr.Number(label="🕒 Idle Hours", value=0, minimum=0)
                with gr.Row():
                    freq = gr.Number(label="📈 Movement Frequency", value=0, minimum=0)
                    cost = gr.Number(label="💰 Cost per Hour",     value=0, minimum=0)
                last           = gr.Textbox(label="🛠️ Last Maintenance Date (YYYY-MM-DD)", placeholder="Optional")
                submit_btn     = gr.Button("🚀 Submit", variant="primary")
                clear_btn      = gr.Button("🧹 Clear")
                result_txt     = gr.Markdown(elem_id="result-box")
                report_file    = gr.File(label="📃 Download PDF Report")
                submit_btn.click(
                    fn=manual_input,
                    inputs=[equipment_dropdown, project_dropdown, usage, idle, freq, cost, last, ai_dropdown],
                    outputs=[result_txt, report_file]  # Only PDF output here now
                )
                clear_btn.click(lambda: ("", None), None, [result_txt, report_file])
        with gr.TabItem("CSV Upload"):
            with gr.Group():
                csv_file   = gr.File(label="📂 Upload CSV file", file_types=[".csv"])
                with gr.Row():
                    upload_btn = gr.Button("🚀 Upload", variant="primary")
                    clear_btn  = gr.Button("🧹 Clear")
                csv_output = gr.Markdown(label="📄 Batch Upload Results", elem_id="result-box")
                batch_pdf = gr.File(label="📃 Download Batch PDF Report", file_types=[".pdf"])
                upload_btn.click(
                    fn=batch_upload,
                    inputs=csv_file,
                    outputs=[csv_output, batch_pdf]
                )
                clear_btn.click(
                    lambda: (None, "", None),  # Reset file input, output markdown, and pdf file output
                    None,
                    [csv_file, csv_output, batch_pdf]
                )
    app.css = """
.gradio-container { background-color: #ffffff !important; }
#app-title { text-align: center !important; }
.gradio-container .gr-group { background-color: #d3d3d3 !important; padding: 20px; border: 3px solid #d3d3d3 !important; border-radius: 10px; }
#result-box { border: 3px solid #d3d3d3 !important; border-radius: 10px; padding: 10px; background: #f9f9f9; white-space: pre-line; }
"""
if __name__ == "__main__":
    app.launch()