src/app.py

import streamlit as st
import os
import re
from models.frame_extractor import extract_frame_timestamp
from models.bounding_box_extractor import extract_bounding_box
from models.catchphrase_extractor import extract_catchphrase
from models.bet_extractor import extract_bet_amount
from utils import (
    extract_frame,
    center_bbox_in_circle,
    overlay_image_bottom_right,
    overlay_image_top_left,
    annotate_image_with_phrase_and_label
)
import numpy as np


def compute_iou(boxA, boxB):
    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[2], boxB[2])
    yB = min(boxA[3], boxB[3])

    interArea = max(0, xB - xA) * max(0, yB - yA)
    if interArea == 0:
        return 0.0

    boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])

    iou = interArea / float(boxAArea + boxBArea - interArea)
    return iou

def extract_voted_bounding_box(image_path, description, num_attempts=5, iou_threshold=0.5):
    bboxes = []

    # Extract multiple bounding boxes
    for _ in range(num_attempts):
        response = extract_bounding_box(image_path, description)
        match = re.search(r'\((\d+),\s*(\d+),\s*(\d+),\s*(\d+)\)', response)
        if match:
            bbox = tuple(map(int, match.groups()))
            bboxes.append(bbox)

    if not bboxes:
        return None

    # Group boxes by IoU
    groups = []
    used = set()

    for i in range(len(bboxes)):
        if i in used:
            continue
        group = [bboxes[i]]
        used.add(i)
        for j in range(i+1, len(bboxes)):
            if j in used:
                continue
            if compute_iou(bboxes[i], bboxes[j]) > iou_threshold:
                group.append(bboxes[j])
                used.add(j)
        if len(group) >= 3:
            groups.append(group)

    if not groups:
        return bboxes[0]  # Fallback to the first bounding box if no groups found

    # Use the group with the most members
    best_group = max(groups, key=len)
    voted_bbox = np.mean(np.array(best_group), axis=0).astype(int)
    return tuple(voted_bbox)

# Set your API key
os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY") if os.getenv("GOOGLE_API_KEY") else st.secrets["GOOGLE_API_KEY"]

os.makedirs("src/tmp", exist_ok=True)

# Paths
video_path = "src/race_vid.mp4"
image_path = "/tmp/extracted_frame.png"

# Streamlit UI
st.set_page_config(page_title="Autonomous Thumbnail Maker", layout="centered")
st.title("🏁 Autonomous Thumbnail Maker")
st.markdown("Generate a social-media-ready racing thumbnail from a video clip using AI.")

st.video(video_path)

if st.button("Generate Thumbnail"):
    with st.spinner("Running AI pipeline..."):

        # FRAME TIMESTAMP
        response, uploaded_obj = extract_frame_timestamp(video_path)
        timestamp_match = re.search(r'\*\*Winning Timestamp:\*\*\s*(\d{2}:\d{2}:\d{2}\.\d{3})', response)
        timestamp = timestamp_match.group(1) if timestamp_match else None

        description_match = re.search(r'\*\*Short description of the winner:\*\*\s*(.*?)\s*(?=\*\*|$)', response, re.DOTALL)
        description = description_match.group(1).strip() if description_match else None

        extract_frame(video_path, timestamp, image_path)

        # BOUNDING BOX
        bbox = extract_voted_bounding_box(image_path, description)

        center_bbox_in_circle(image_path, bbox, image_path)
        overlay_image_bottom_right(image_path, "src/assets/corner.png", image_path)
        overlay_image_top_left(image_path, "src/assets/GetOn_Logo.png", image_path)

        # PHRASES
        catchphrases = extract_catchphrase(video_path)
        phrases = re.findall(r'"(.*?)"', catchphrases)

        # BET LABEL
        bet_amounts = extract_bet_amount(video_path)
        labels = re.findall(r'\*\*Exact Label Text:\*\* "([^"]+)"', bet_amounts)

        phrase = phrases[0] if phrases else "No catchphrase found"
        label = labels[0] if labels else "No bet label found"

        annotate_image_with_phrase_and_label(image_path, image_path, phrase, label)

        st.success("Thumbnail generated!")
        st.image(image_path, caption="Generated Thumbnail", use_container_width=True)

        with open(image_path, "rb") as f:
            st.download_button("📥 Download Thumbnail", f, file_name="thumbnail.png", mime="image/png")