import os
import random
import string
import time
import traceback  # For detailed error logging
import uuid  # For unique filenames

import matplotlib.font_manager as fm
import numpy as np
from PIL import Image, ImageDraw, ImageFont, ImageFilter
from dotenv import load_dotenv
from flask import Flask, request, render_template, send_from_directory, url_for, flash, redirect
from moviepy.editor import ImageSequenceClip  # Use .editor for newer moviepy versions

# --- Mistral AI Integration ---
MISTRAL_AVAILABLE = False
MISTRAL_API_KEY = None
try:
    # --- Mistral AI Integration ---
    from mistralai import UserMessage, SystemMessage, Mistral

    load_dotenv() # Load environment variables from .env file
    MISTRAL_API_KEY = os.environ.get("MISTRAL_API_KEY")
    if MISTRAL_API_KEY:
        MISTRAL_AVAILABLE = True
    else:
        print("Warning: MISTRAL_API_KEY not found in environment variables. AI disabled.")
except ImportError:
    UserMessage = None
    Mistral = None
    print("Warning: Mistral AI library not found. AI text generation disabled.")
    print("Install it using: pip install mistralai python-dotenv")
except Exception as _:
    print(f"Warning: Error initializing Mistral AI: {_}. AI disabled.")

# --- Flask App Setup ---
app = Flask(__name__)
app.config['SECRET_KEY'] = os.urandom(24) # Needed for flash messages (optional but good practice)
app.config['UPLOAD_FOLDER'] = '/tmp/output'
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
app.config['FONT_DIR'] = 'fonts'
app.config['MAX_CONTENT_LENGTH'] = 5 * 1024 * 1024 # Limit upload size if adding file uploads later (5MB example)

# Ensure output and font directories exist
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
os.makedirs(app.config['FONT_DIR'], exist_ok=True)

# --- Configuration Parameters ---

# Video settings
WIDTH = 1024
HEIGHT = 1024
FPS = 10
DURATION_SECONDS = 5

# Text & Highlighting settings
HIGHLIGHTED_TEXT = "Mother of Dragons"
HIGHLIGHT_COLOR = "yellow"  # Pillow color name or hex code
TEXT_COLOR = "black"
BACKGROUND_COLOR = "white"
FONT_SIZE_RATIO = 0.05  # Adjusted slightly for multi-line potentially
MIN_LINES = 7  # Min number of text lines per frame
MAX_LINES = 10  # Max number of text lines per frame
VERTICAL_SPREAD_FACTOR = 1.5  # Multiplier for line height (1.0 = tight, 1.5 = looser)

# AI Text Generation Settings
AI_GENERATION_ENABLED = MISTRAL_AVAILABLE  # Auto-disable if library missing
UNIQUE_TEXT_COUNT = 2  # Number of unique text snippets to generate/pre-pool
MISTRAL_MODEL = "mistral-large-latest"  # Or choose another suitable model
# !! IMPORTANT: Load API Key securely !!
MISTRAL_API_KEY = os.environ.get("MISTRAL_API_KEY")

# Effect settings
BLUR_TYPE = 'radial'  # Options: 'gaussian', 'radial'
BLUR_RADIUS = 4.0  # Gaussian blur radius, or the radius OUTSIDE which radial blur starts fading strongly
RADIAL_SHARPNESS_RADIUS_FACTOR = 0.3  # For 'radial': Percentage of min(W,H) to keep perfectly sharp around center

# Font settings
FONT_DIR = "fonts"  # Dedicated font folder recommended
MAX_FONT_RETRIES_PER_FRAME = 5
# Generate random words only using ASCII lowercase for fallback/disabled AI
FALLBACK_CHAR_SET = string.ascii_lowercase + " "

# --- Helper Functions (Mostly unchanged from original script) ---

class FontLoadError(Exception): pass
class FontDrawError(Exception): pass

def get_random_font(font_paths, exclude_list=None):
    """Selects a random font file path from the list, avoiding excluded ones."""
    available_fonts = list(set(font_paths) - set(exclude_list or []))
    if not available_fonts:
        try:
            # More robust fallback finding sans-serif
            prop = fm.FontProperties(family='sans-serif')
            fallback_path = fm.findfont(prop, fallback_to_default=True)
            if fallback_path:
                 print(f"Warning: No usable fonts found from list/system. Using fallback: {fallback_path}")
                 return fallback_path
            else:
                 # If even matplotlib fallback fails (unlikely but possible)
                 print("ERROR: No fonts found in specified dir, system, or fallback. Cannot proceed.")
                 return None
        except Exception as e:
            print(f"ERROR: Font fallback mechanism failed: {e}. Cannot proceed.")
            return None
    return random.choice(available_fonts)

# Fallback random text generator
def generate_random_words(num_words):
    """Generates a string of random 'words' using only FALLBACK_CHAR_SET."""
    words = []
    for _ in range(num_words):
        length = random.randint(3, 8)
        word = ''.join(random.choice(FALLBACK_CHAR_SET.replace(" ", "")) for i in range(length))
        words.append(word)
    return " ".join(words)

def generate_random_text_snippet(highlighted_text, min_lines, max_lines):
    """Generates multiple lines of random text, ensuring MIN_LINES."""
    # Ensure we generate at least min_lines
    num_lines = random.randint(max(1, min_lines), max(min_lines, max_lines))  # Ensure at least min_lines generated
    highlight_line_index = random.randint(0, num_lines - 1)
    lines = []
    min_words_around = 2
    max_words_around = 6
    for i in range(num_lines):
        if i == highlight_line_index:
            words_before = generate_random_words(random.randint(min_words_around, max_words_around))
            words_after = generate_random_words(random.randint(min_words_around, max_words_around))
            lines.append(f"{words_before} {highlighted_text} {words_after}")
        else:
            lines.append(generate_random_words(random.randint(max_words_around, max_words_around * 2)))

    # Double-check final line count (should always pass with the adjusted randint)
    if len(lines) < min_lines:
        print(f"Warning: Random generator created only {len(lines)} lines (min: {min_lines}). This shouldn't happen.")
        return None, -1  # Treat as failure if check fails unexpectedly

    return lines, highlight_line_index

# Mistral AI Text Generation Function
def generate_ai_text_snippet(client, model, highlighted_text, min_lines, max_lines):
    """Generates a text snippet using Mistral AI containing the highlighted text."""
    target_lines = random.randint(min_lines, max_lines)
    prompt = (
        f"Generate a text block of approximately {target_lines} distinct lines (aim for at least {min_lines}). "
        f"One of the lines MUST contain the exact phrase: '{highlighted_text}'. "
        f"The surrounding text should be thematically related to '{highlighted_text}' (e.g., fantasy, power, dragons, leadership). "
        f"Ensure the phrase '{highlighted_text}' fits naturally within its line. "
        f"Format the output ONLY as the text lines, each separated by a single newline character. Do not add any extra explanations or formatting."
        # f"Example line containing the phrase: '...they bowed before the {highlighted_text}, their new queen...'"
    )

    try:
        messages = [UserMessage(content=prompt)]
        chat_response = client.chat.complete(model=model, messages=messages,
                                             temperature=0.5, max_tokens=300)
        content = chat_response.choices[0].message.content.strip()

        # Basic cleanup: remove potential empty lines
        lines = [line for line in content.split('\n') if line.strip()]

        # --- CRITICAL CHECK: Ensure minimum lines ---
        if len(lines) < min_lines:
            print(
                f"Warning: AI returned only {len(lines)} valid lines (minimum requested: {min_lines}). Retrying generation.")
            return None, -1  # Indicate failure due to insufficient lines

        # Find the highlight line
        highlight_line_index = -1
        for i, line in enumerate(lines):
            if highlighted_text in line:
                highlight_line_index = i
                break

        if highlight_line_index == -1:
            print(f"Warning: AI response did not contain the exact phrase '{highlighted_text}'.")
            # Optionally try to insert it into a random line? Or just fail.
            # Let's fail for now to ensure the highlight is always from AI context
            return None, -1  # Indicate failure

        return lines, highlight_line_index
    except Exception as e:
        print(f"An unexpected error occurred during AI text generation: {e}")
        return None, -1  # Indicate failure

def create_radial_blur_mask(width, height, center_x, center_y, sharp_radius, fade_radius):
    """Creates a grayscale mask for radial blur (sharp center, fades out)."""
    mask = Image.new('L', (width, height), 0)
    draw = ImageDraw.Draw(mask)
    draw.ellipse(
        (center_x - sharp_radius, center_y - sharp_radius,
         center_x + sharp_radius, center_y + sharp_radius),
        fill=255
    )
    # Gaussian blur the sharp circle mask for a smooth falloff
    # Ensure fade radius is larger than sharp radius
    blur_amount = max(0.1, (fade_radius - sharp_radius) / 3.5)  # Adjusted divisor for smoothness
    mask = mask.filter(ImageFilter.GaussianBlur(radius=blur_amount))
    return mask


def create_text_image_frame(width, height, text_lines, highlight_line_index, highlighted_text,
                            font_path, font_size, text_color, bg_color, highlight_color,
                            blur_type, blur_radius, radial_sharp_radius_factor, vertical_spread_factor):
    """Creates a single frame image with centered highlight and multi-line text."""

    # --- Font Loading ---
    try:
        font = ImageFont.truetype(font_path, font_size)
        bold_font = font  # Start with regular as fallback
        # Simple bold variant check (can be improved)
        common_bold_suffixes = ["bd.ttf", "-Bold.ttf", "b.ttf", "_Bold.ttf", " Bold.ttf"]
        base_name, ext = os.path.splitext(font_path)
        for suffix in common_bold_suffixes:
            potential_bold_path = base_name.replace("Regular", "").replace("regular",
                                                                           "") + suffix  # Try removing 'Regular' too
            if os.path.exists(potential_bold_path):
                try:
                    bold_font = ImageFont.truetype(potential_bold_path, font_size)
                    # print(f"    Using bold variant: {os.path.basename(potential_bold_path)}") # Debug
                    break  # Use the first one found
                except IOError:
                    continue  # Try next suffix if loading fails
            # Check without removing Regular if first checks failed
            potential_bold_path = base_name + suffix
            if os.path.exists(potential_bold_path):
                try:
                    bold_font = ImageFont.truetype(potential_bold_path, font_size)
                    # print(f"    Using bold variant: {os.path.basename(potential_bold_path)}") # Debug
                    break
                except IOError:
                    continue

    except IOError as e:
        raise FontLoadError(f"Failed to load font: {font_path}") from e
    except Exception as e:  # Catch other potential font loading issues
        raise FontLoadError(f"Unexpected error loading font {font_path}: {e}") from e

    # --- Calculations ---
    try:
        # Line height using getmetrics()
        try:
             ascent, descent = font.getmetrics()
             metric_height = ascent + abs(descent)
             line_height = int(metric_height * vertical_spread_factor)
        except AttributeError:
             bbox_line_test = font.getbbox("Ay", anchor="lt")
             line_height = int((bbox_line_test[3] - bbox_line_test[1]) * vertical_spread_factor)
        if line_height <= font_size * 0.8:
            line_height = int(font_size * 1.2 * vertical_spread_factor)

        # BOLD font metrics for final highlight placement
        highlight_width_bold = bold_font.getlength(highlighted_text)
        highlight_bbox_h = bold_font.getbbox(highlighted_text, anchor="lt")
        highlight_height_bold = highlight_bbox_h[3] - highlight_bbox_h[1]
        if highlight_width_bold <= 0 or highlight_height_bold <= 0:
             highlight_height_bold = int(font_size * 1.1)
             if highlight_width_bold <=0: highlight_width_bold = len(highlighted_text) * font_size * 0.6

        # Target position for the TOP-LEFT of the final BOLD highlight text (CENTERED)
        highlight_target_x = (width - highlight_width_bold) / 2
        highlight_target_y = (height - highlight_height_bold) / 2

        # Block start Y calculated relative to the centered highlight's top
        block_start_y = highlight_target_y - (highlight_line_index * line_height)

        # Get Prefix and Suffix for background alignment
        highlight_line_full_text = text_lines[highlight_line_index]
        prefix_text = ""
        suffix_text = "" # Also get suffix now
        highlight_found_in_line = False
        try:
            start_index = highlight_line_full_text.index(highlighted_text)
            end_index = start_index + len(highlighted_text)
            prefix_text = highlight_line_full_text[:start_index]
            suffix_text = highlight_line_full_text[end_index:]
            highlight_found_in_line = True
        except ValueError: pass # Treat line normally if not found

        # Measure Prefix Width using REGULAR font (for background positioning)
        prefix_width_regular = font.getlength(prefix_text)
        # Calculate the required starting X for the background highlight line string
        # This is the coordinate used for drawing the *full string* in the background
        bg_highlight_line_start_x = highlight_target_x - prefix_width_regular

    except AttributeError: raise FontDrawError(f"Font lacks methods.")
    except Exception as e: raise FontDrawError(f"Measurement fail: {e}") from e

    # --- Base Image Drawing (Draw FULL lines, use offset for HL line) ---
    # Render onto img_base normally first
    img_base = Image.new('RGB', (width, height), color=bg_color)
    draw_base = ImageDraw.Draw(img_base)
    try:
        current_y = block_start_y
        for i, line in enumerate(text_lines):
            line_x = 0.0
            if i == highlight_line_index and highlight_found_in_line:
                line_x = bg_highlight_line_start_x
            else:
                line_width = font.getlength(line)
                line_x = (width - line_width) / 2
            draw_base.text((line_x, current_y), line, font=font, fill=text_color, anchor="lt")
            current_y += line_height
    except Exception as e: raise FontDrawError(f"Base draw fail: {e}") from e

    # --- Apply Blur (with padding for Gaussian to avoid edge clipping) ---
    img_blurred = None # Initialize
    padding_for_blur = int(blur_radius * 3) # Padding based on blur radius

    if blur_type == 'gaussian' and blur_radius > 0:
        try:
            # Create larger canvas
            padded_width = width + 2 * padding_for_blur
            padded_height = height + 2 * padding_for_blur
            img_padded = Image.new('RGB', (padded_width, padded_height), color=bg_color)
            # Paste original centered onto padded canvas
            img_padded.paste(img_base, (padding_for_blur, padding_for_blur))
            # Blur the padded image
            img_padded_blurred = img_padded.filter(ImageFilter.GaussianBlur(radius=blur_radius))
            # Crop the center back to original size
            img_blurred = img_padded_blurred.crop((padding_for_blur, padding_for_blur,
                                                  padding_for_blur + width, padding_for_blur + height))
        except Exception as e:
            print(f"Error during padded Gaussian blur: {e}. Falling back to direct blur.")
            img_blurred = img_base.filter(ImageFilter.GaussianBlur(radius=blur_radius)) # Fallback


    elif blur_type == 'radial' and blur_radius > 0:
        # For radial, we need img_sharp. Let's try drawing it *in parts* for reliability
        # as the padded blur trick doesn't apply directly here.
        img_sharp = Image.new('RGB', (width, height), color=bg_color)
        draw_sharp = ImageDraw.Draw(img_sharp)
        try:
            current_y = block_start_y
            for i, line in enumerate(text_lines):
                if i == highlight_line_index and highlight_found_in_line:
                    # --- Draw Sharp Highlight Line in Parts ---
                    # Calculate positions relative to the *final* centered highlight target
                    prefix_x = highlight_target_x - prefix_width_regular
                    # Use REGULAR font for the sharp layer (it's just for the mask)
                    draw_sharp.text((prefix_x, current_y), prefix_text, font=font, fill=text_color, anchor="lt")
                    # Highlight part itself starts at highlight_target_x
                    highlight_width_regular = font.getlength(highlighted_text) # Width in regular font
                    draw_sharp.text((highlight_target_x, current_y), highlighted_text, font=font, fill=text_color, anchor="lt")
                    # Suffix starts after the regular highlight width
                    suffix_x = highlight_target_x + highlight_width_regular
                    draw_sharp.text((suffix_x, current_y), suffix_text, font=font, fill=text_color, anchor="lt")
                else:
                    # Draw non-highlight lines centered normally
                    line_width = font.getlength(line)
                    line_x = (width - line_width) / 2
                    draw_sharp.text((line_x, current_y), line, font=font, fill=text_color, anchor="lt")
                current_y += line_height
        except Exception as e:
             raise FontDrawError(f"Failed sharp text draw (parts): {e}") from e

        # Composite blurred base and sharp center
        # Base image (img_base) still uses the offset drawing method for full line
        img_fully_blurred = img_base.filter(ImageFilter.GaussianBlur(radius=blur_radius * 1.5))
        sharp_center_radius = min(width, height) * radial_sharp_radius_factor
        fade_radius = sharp_center_radius + max(width, height) * 0.15
        mask = create_radial_blur_mask(width, height, width / 2, height / 2, sharp_center_radius, fade_radius)
        img_blurred = Image.composite(img_sharp, img_fully_blurred, mask)

    else: # No blur
        img_blurred = img_base.copy()


    # --- Final Image: Draw ONLY Highlight Rectangle & Centered BOLD Text ---
    final_img = img_blurred # Start with the blurred/composited image
    draw_final = ImageDraw.Draw(final_img)
    try:
        # 1. Draw highlight rectangle (centered using bold metrics)
        padding = font_size * 0.10
        draw_final.rectangle(
            [
                (highlight_target_x - padding, highlight_target_y - padding),
                (highlight_target_x + highlight_width_bold + padding, highlight_target_y + highlight_height_bold + padding)
            ],
            fill=highlight_color
        )

        # 2. Draw ONLY the SHARP highlight text using BOLD font at the *perfectly centered* position
        draw_final.text(
            (highlight_target_x, highlight_target_y),
            highlighted_text,
            font=bold_font, # Use BOLD font
            fill=text_color,
            anchor="lt"
        )
        # *** No prefix/suffix drawing here ***

    except Exception as e:
         raise FontDrawError(f"Failed final highlight draw: {e}") from e

    return final_img


# --- Core Video Generation Logic (Adapted from main) ---
def generate_video(params):
    """Generates the video based on input parameters."""

    # Unpack parameters from the dictionary passed by the Flask route
    width = params['width']
    height = params['height']
    fps = params['fps']
    duration_seconds = params['duration']
    highlighted_text = params['highlighted_text']
    highlight_color = params['highlight_color']
    text_color = params['text_color']
    background_color = params['background_color']
    blur_type = params['blur_type']
    blur_radius = params['blur_radius']
    ai_enabled = params['ai_enabled']
    font_dir = app.config['FONT_DIR'] # Use font dir from Flask config

    # Hardcoded or derived settings from original script
    font_size_ratio = 0.05 # Could be made a parameter
    min_lines = 7
    max_lines = 10
    vertical_spread_factor = 1.5
    radial_sharp_radius_factor = 0.3
    unique_text_count = 2 # Generate a couple of options per request
    mistral_model = "mistral-large-latest" # Could be param

    print(f"Starting video generation with params: {params}")

    mistral_client = None
    if ai_enabled and MISTRAL_AVAILABLE:
        try:
            mistral_client = Mistral(api_key=MISTRAL_API_KEY)
            print("Mistral AI client initialized.")
        except Exception as e:
            print(f"Error initializing Mistral client: {e}. Disabling AI for this request.")
            ai_enabled = False # Disable AI if client fails
    elif ai_enabled and not MISTRAL_AVAILABLE:
        print("AI was requested but is not available (check API key/library). Using random text.")
        ai_enabled = False

    # --- Font Discovery ---
    font_paths = []
    if font_dir and os.path.isdir(font_dir):
        print(f"Looking for fonts in specified directory: {font_dir}")
        for filename in os.listdir(font_dir):
            if filename.lower().endswith((".ttf", ".otf")):
                font_paths.append(os.path.join(font_dir, filename))
    else:
        print("FONT_DIR not specified or invalid, searching system fonts...")
        try:
            # Limit search to common locations if possible, or search all
            font_paths = fm.findSystemFonts(fontpaths=None, fontext='ttf')
            # font_paths.extend(fm.findSystemFonts(fontpaths=None, fontext='otf'))
        except Exception as e:
            print(f"Error finding system fonts: {e}")

    if not font_paths:
        print("ERROR: No fonts found in font dir or system. Cannot proceed.")
        return None, "No fonts found. Please add fonts to the 'fonts' directory or install system fonts."

    print(f"Found {len(font_paths)} potential fonts.")

    # --- Pre-generate Text Snippets ---
    text_snippets_pool = []
    print(f"Generating text snippets (AI: {ai_enabled})...")

    generation_attempts = 0
    max_generation_attempts = unique_text_count * 4 # Allow more attempts

    while len(text_snippets_pool) < unique_text_count and generation_attempts < max_generation_attempts:
        generation_attempts += 1
        if ai_enabled and mistral_client:
            print(f"  Attempting AI generation ({generation_attempts})...")
            lines, hl_index = generate_ai_text_snippet(mistral_client, mistral_model, highlighted_text, min_lines, max_lines)
            if lines is None or hl_index == -1:
                 print("    AI generation failed or invalid. Will retry or use random.")
                 time.sleep(0.5) # Small delay before retry
                 # Fallback to random if AI keeps failing near the end
                 if generation_attempts > max_generation_attempts // 2:
                     print("    AI failed repeatedly, falling back to random for this snippet.")
                     lines, hl_index = generate_random_text_snippet(highlighted_text, min_lines, max_lines)
            else:
                print(f"    AI snippet generated ({len(lines)} lines).")

        else: # Use random if AI disabled or failed fallback
            print("  Generating random text snippet...")
            lines, hl_index = generate_random_text_snippet(highlighted_text, min_lines, max_lines)

        # Add successfully generated snippet to pool
        if lines and hl_index != -1:
             text_snippets_pool.append({"lines": lines, "highlight_index": hl_index})

    if not text_snippets_pool:
        print("ERROR: Failed to generate any text snippets (AI or random).")
        return None, "Failed to generate text content for the video."

    print(f"Generated {len(text_snippets_pool)} text snippets for the pool.")

    # --- Calculate Other Parameters ---
    total_frames = int(fps * duration_seconds)
    # Calculate font size based on height dynamically
    font_size = int(height * font_size_ratio)
    print(f"\nVideo Settings: {width}x{height} @ {fps}fps, {duration_seconds}s ({total_frames} frames)")
    print(f"Text Settings: Highlight='{highlighted_text}', Size={font_size}px")
    print(f"Effect Settings: BlurType='{blur_type}', BlurRadius={blur_radius}, HighlightColor='{highlight_color}'")

    # --- Generate Frames ---
    frames = []
    failed_fonts = set()
    print("\nGenerating frames...")
    frame_num = 0
    while frame_num < total_frames:
        # print(f"  Attempting Frame {frame_num + 1}/{total_frames}") # Can be verbose

        # Select a text snippet and font for this frame
        snippet = random.choice(text_snippets_pool)
        current_lines = snippet["lines"]
        highlight_idx = snippet["highlight_index"]

        font_retries = 0
        frame_generated = False
        while font_retries < MAX_FONT_RETRIES_PER_FRAME:
            current_font_path = get_random_font(font_paths, exclude_list=failed_fonts)
            if current_font_path is None:
                # This now returns None only if EVERYTHING fails, including fallback
                return None, "No usable fonts available after multiple attempts."

            try:
                img = create_text_image_frame(
                    width, height,
                    current_lines, highlight_idx, highlighted_text,
                    current_font_path, font_size,
                    text_color, background_color, highlight_color,
                    blur_type, blur_radius, radial_sharp_radius_factor,
                    vertical_spread_factor
                )

                frame_np = np.array(img)
                frames.append(frame_np)
                frame_generated = True
                # print(f"    Frame {frame_num + 1} generated with font: {os.path.basename(current_font_path)}")
                break # Success, move to next frame attempt

            except (FontLoadError, FontDrawError) as e:
                print(f"    Warning: Font '{os.path.basename(current_font_path)}' failed for frame {frame_num + 1}. ({e}). Retrying with another font.")
                failed_fonts.add(current_font_path)
                font_retries += 1
                # Check if we've run out of fonts to try for this frame
                if len(failed_fonts) >= len(font_paths):
                     print(f"    ERROR: All available fonts failed for frame {frame_num + 1}. Trying system fallback once more.")
                     # Try the matplotlib fallback directly if list exhausted
                     fallback_font = get_random_font([], exclude_list=failed_fonts) # Trigger fallback explicitly
                     if fallback_font and fallback_font not in failed_fonts:
                          failed_fonts.add(fallback_font) # Add it so we don't retry infinitely
                          font_retries = 0 # Reset retries for the fallback font
                          print(f"    Attempting frame {frame_num + 1} with fallback font: {fallback_font}")
                          continue # Re-enter the loop to try drawing with fallback
                     else:
                          print(f"    ERROR: Even fallback font failed or wasn't found. Skipping frame {frame_num + 1}.")
                          frame_generated = False # Mark as not generated
                          break # Break font retry loop for this frame

            except Exception as e:
                print(f"    ERROR: Unexpected error generating frame {frame_num + 1} with font {os.path.basename(current_font_path)}: {e}")
                traceback.print_exc() # Log full error
                failed_fonts.add(current_font_path)
                font_retries += 1

        if not frame_generated:
            print(f"ERROR: Failed to generate Frame {frame_num + 1} after {MAX_FONT_RETRIES_PER_FRAME} font attempts. Stopping video generation.")
            # Decide whether to stop entirely or just make a shorter video
            # For a web app, stopping might be better than returning a broken/short video.
            return None, f"Failed to generate frame {frame_num + 1}. Font issues likely. Check font compatibility."
            # break # Or use break to create a shorter video

        frame_num += 1
        # Add progress update for long renders
        if frame_num % (total_frames // 10) == 0 or frame_num == total_frames: # Update every 10%
             print(f"  Progress: {frame_num}/{total_frames} frames generated...")


    # --- Create Video ---
    if not frames:
        print("ERROR: No frames were generated. Cannot create video.")
        return None, "No frames were generated, possibly due to persistent font errors."

    if len(frames) < total_frames:
        print(f"Warning: Only {len(frames)}/{total_frames} frames were generated due to errors. Video will be shorter.")

    # Generate unique filename
    unique_id = uuid.uuid4()
    output_filename = f"text_match_cut_{unique_id}.mp4"
    output_path = os.path.join(app.config['UPLOAD_FOLDER'], output_filename)

    print(f"\nCompiling video to {output_path}...")
    try:
        # Ensure frames is a list of numpy arrays
        if not isinstance(frames[0], np.ndarray):
             frames = [np.array(f) for f in frames]

        clip = ImageSequenceClip(frames, fps=fps)

        # Write video file using recommended settings
        # logger='bar' might not work well in web server logs, use None or default
        # Specify audio=False if there's no audio track
        # threads can speed up encoding, preset affects quality/speed balance
        clip.write_videofile(output_path,
                             codec='libx264', # Good compatibility
                             preset='medium', # Balance speed/quality (ultrafast, superfast, veryfast, faster, fast, medium, slow, slower, veryslow)
                             fps=fps,
                             threads=max(1, os.cpu_count() // 2), # Use half CPU cores
                             logger=None, # Avoid progress bar in server logs
                             audio=False) # Explicitly no audio

        clip.close() # Release resources
        print(f"\nVideo saved successfully as '{output_filename}'")

        # Optionally list failed fonts
        if failed_fonts:
            print("\nFonts that caused errors during generation:")
            for ff in sorted(list(failed_fonts)):
                print(f" - {os.path.basename(ff)}")

        return output_filename, None # Return filename on success, no error

    except Exception as e:
        print(f"\nError during video writing: {e}")
        traceback.print_exc()
        error_message = f"Error during video writing: {e}. Check server logs and FFmpeg installation/codec support (libx264)."
        # Clean up potentially partially written file
        if os.path.exists(output_path):
            try:
                os.remove(output_path)
            except OSError:
                pass # Ignore cleanup error
        return None, error_message


# --- Flask Routes ---

@app.route('/', methods=['GET'])
def index():
    """Renders the main form page."""
    # Pass mistral availability to the template
    return render_template('index.html', mistral_available=MISTRAL_AVAILABLE)

@app.route('/generate', methods=['POST'])
def generate():
    """Handles form submission, triggers video generation."""
    try:
        params = {
            'width': request.form.get('width', default=1024, type=int),
            'height': request.form.get('height', default=1024, type=int),
            'fps': request.form.get('fps', default=10, type=int),
            'duration': request.form.get('duration', default=5, type=int),
            'highlighted_text': request.form.get('highlighted_text', default="Missing Text"),
            'highlight_color': request.form.get('highlight_color', default='#FFFF00'),
            'text_color': request.form.get('text_color', default='#000000'),
            'background_color': request.form.get('background_color', default='#FFFFFF'),
            'blur_type': request.form.get('blur_type', default='gaussian'),
            'blur_radius': request.form.get('blur_radius', default=4.0, type=float),
            'ai_enabled': request.form.get('ai_enabled') == 'true' and MISTRAL_AVAILABLE, # Only enable if checkbox checked AND available
        }

        # Basic Input Validation (Example)
        if not params['highlighted_text']:
            flash('Highlighted text cannot be empty.', 'error')
            return redirect(url_for('index'))
        if not (1 <= params['fps'] <= 60):
            flash('FPS must be between 1 and 60.', 'error')
            return redirect(url_for('index'))
        if not (1 <= params['duration'] <= 60): # Limit duration
             flash('Duration must be between 1 and 60 seconds.', 'error')
             return redirect(url_for('index'))
        if not (256 <= params['width'] <= 4096) or not (256 <= params['height'] <= 4096):
             flash('Width and Height must be between 256 and 4096 pixels.', 'error')
             return redirect(url_for('index'))


        # --- Trigger the generation ---
        generated_filename, error = generate_video(params)

        if error:
            # Render index page again, displaying the error
            return render_template('index.html', error=error, mistral_available=MISTRAL_AVAILABLE)
        else:
            # Render index page again, providing the download link
            return render_template('index.html', filename=generated_filename, mistral_available=MISTRAL_AVAILABLE)

    except Exception as e:
        print(f"An unexpected error occurred in /generate route: {e}")
        traceback.print_exc()
        return render_template('index.html', error=f"An unexpected server error occurred: {e}", mistral_available=MISTRAL_AVAILABLE)


@app.route('/output/<filename>')
def download_file(filename):
    """Serves the generated video file for download."""
    try:
        # Security: Ensure filename is safe and only serves from the UPLOAD_FOLDER
        return send_from_directory(app.config["UPLOAD_FOLDER"], filename, as_attachment=True)
    except FileNotFoundError:
         flash('Error: File not found. It might have been deleted or generation failed.', 'error')
         return redirect(url_for('index'))
    except Exception as e:
         print(f"Error serving file {filename}: {e}")
         flash('An error occurred while trying to serve the file.', 'error')
         return redirect(url_for('index'))

# --- Main Execution ---
if __name__ == '__main__':
    print(f"Mistral AI Available: {MISTRAL_AVAILABLE}")
    # Use host='0.0.0.0' to make accessible on your network (use with caution)
    # debug=True automatically reloads on code changes, but disable for production
    app.run(debug=True, host='127.0.0.1', port=5000)