import streamlit as st
import pandas as pd
import cv2
import easyocr
import tempfile
import requests
import os
from datetime import datetime

# ✅ Google Drive API Key and Folder ID
API_KEY = "AIzaSyDojJrpauA0XZtCCDUuo9xeQHZQamYKsC4"
FOLDER_ID = "1egelZ7ZyHBNcXmtObX0CWfr_Q_ilfX9p"

LOG_FILE = "vehicle_log.csv"
FRAME_SKIP = 15  # Reduced to improve detection
reader = easyocr.Reader(['en'], gpu=False)

st.title("🚓 Improved Vehicle Detection from Google Drive CCTV")

# Ensure log file exists
if not os.path.exists(LOG_FILE):
    pd.DataFrame(columns=["Vehicle Number", "Timestamp", "Video File"]).to_csv(LOG_FILE, index=False)

def list_drive_files(folder_id):
    url = f"https://www.googleapis.com/drive/v3/files?q='{folder_id}'+in+parents+and+(mimeType='video/mp4'+or+mimeType='video/avi')&key={API_KEY}&fields=files(id,name)"
    resp = requests.get(url)
    if resp.status_code != 200:
        st.error(f"Drive API Error: {resp.text}")
        return []
    return resp.json().get("files", [])

def download_drive_video(file_id):
    download_url = f"https://www.googleapis.com/drive/v3/files/{file_id}?alt=media&key={API_KEY}"
    resp = requests.get(download_url, stream=True)
    if resp.status_code != 200:
        return None
    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
    for chunk in resp.iter_content(chunk_size=8192):
        if chunk:
            temp.write(chunk)
    temp.close()
    return temp.name

def process_video(video_path, video_name, log_df):
    cap = cv2.VideoCapture(video_path)
    frame_num = 0
    new_logs = []
    detected_this_video = set()

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        if frame_num % FRAME_SKIP == 0:
            resized = cv2.resize(frame, (640, 360))
            gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
            results = reader.readtext(gray)

            for (_, text, _) in results:
                text = text.replace(" ", "").upper()
                if len(text) >= 6 and any(char.isdigit() for char in text):
                    if text not in detected_this_video:
                        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                        new_logs.append([text, timestamp, video_name])
                        detected_this_video.add(text)
                        st.success(f"Detected: {text} at {timestamp}")
        frame_num += 1
    cap.release()
    return new_logs

# Step 1: Download and process videos
files = list_drive_files(FOLDER_ID)
if files:
    try:
        log_df = pd.read_csv(LOG_FILE)
    except pd.errors.EmptyDataError:
        log_df = pd.DataFrame(columns=["Vehicle Number", "Timestamp", "Video File"])

    all_new_logs = []

    for file in files:
        st.info(f"Processing: {file['name']}")
        local_path = download_drive_video(file['id'])
        if local_path:
            new_logs = process_video(local_path, file['name'], log_df)
            all_new_logs.extend(new_logs)
            os.remove(local_path)

    if all_new_logs:
        pd.DataFrame(all_new_logs, columns=["Vehicle Number", "Timestamp", "Video File"]).to_csv(
            LOG_FILE, mode='a', index=False, header=not os.path.exists(LOG_FILE))
        st.success("✅ Logs updated.")
else:
    st.warning("No video files found or API error.")

# Step 2: Analyze logs
if os.path.exists(LOG_FILE):
    try:
        df = pd.read_csv(LOG_FILE)
        df["Timestamp"] = pd.to_datetime(df["Timestamp"])

        entries = df.sort_values("Timestamp").groupby("Vehicle Number").first()
        exits = df.sort_values("Timestamp").groupby("Vehicle Number").last()

        summary = pd.DataFrame()
        summary["Vehicle Number"] = entries.index
        summary["Entry Time"] = entries["Timestamp"]
        summary["Exit Time"] = exits["Timestamp"]
        summary["Duration (minutes)"] = (summary["Exit Time"] - summary["Entry Time"]).dt.total_seconds() / 60
        summary["Overstay Alert"] = summary["Duration (minutes)"] > (24 * 60)
        summary["Checked Out"] = summary["Entry Time"] != summary["Exit Time"]

        st.subheader("📊 Vehicle Summary (Duration in Minutes)")
        st.dataframe(summary)
    except Exception as e:
        st.error(f"Failed to analyze logs: {e}")