| import gradio as gr | |
| import requests | |
| import os | |
| import pickle | |
| headers = { | |
| "X-RapidAPI-Key": os.getenv('TIKTOK_API'), | |
| "X-RapidAPI-Host": "tiktok-scraper7.p.rapidapi.com" | |
| } | |
| with open('model_scaler.pkl', 'rb') as f: | |
| scaler = pickle.load(f) | |
| with open('model_random_forest.pkl', 'rb') as f: | |
| model = pickle.load(f) | |
| def get_video_details(link): | |
| response = requests.get("https://tiktok-scraper7.p.rapidapi.com/", headers=headers, params={"url":link,"hd":"1"}) | |
| video_info = response.json() | |
| length = video_info['data']['duration'] | |
| video_like = int(video_info['data']['digg_count']) | |
| video_share = int(video_info['data']['share_count']) | |
| video_comment = int(video_info['data']['share_count']) | |
| video_view = int(video_info['data']['comment_count']) | |
| video_engagement = video_like+video_share+video_comment+video_view | |
| play_url = video_info['data']['play'] | |
| user_id = video_info['data']['author']['id'] | |
| music_id = video_info['data']['music_info']['id'] | |
| create_time = video_info['data']['create_time'] | |
| response = requests.get("https://tiktok-scraper7.p.rapidapi.com/user/info", headers=headers, params={"user_id":user_id}) | |
| user_info = response.json() | |
| follower = user_info['data']['stats']['followerCount'] | |
| total_likes = user_info['data']['stats']['heartCount'] | |
| total_video = user_info['data']['stats']['videoCount'] | |
| markdown_for_showing = f"""**Video Information** | |
| - **Likes**: {video_like} | |
| - **Shares**: {video_share} | |
| - **Comments**: {video_comment} | |
| - **Views**: {video_view} | |
| """ | |
| html_to_play = f'<video src={play_url} />' | |
| return html_to_play,markdown_for_showing, length, follower, total_likes, total_video, gr.update(visible=True) | |
| def visible_component(component): | |
| return gr.update(visible=True) | |
| def predict(length,followers,total_likes,total_videos,days_since_debut): | |
| data = scaler.transform([[length,followers,total_likes,total_videos,days_since_debut]]) | |
| return model.predict(data) | |
| css = """ | |
| button { | |
| height: 100%; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Row(variant='compact',equal_height=True): | |
| with gr.Column(scale=4): | |
| video_url = gr.Textbox(label="Enter a Tiktok Video URL",placeholder="https://www.tiktok.com/...") | |
| with gr.Column(scale=1): | |
| submit_button = gr.Button("Get Data from URL") | |
| second_row = gr.Row(variant='compact',equal_height=True, visible=False) | |
| with second_row: | |
| with gr.Column(scale=1): | |
| tiktok_video = gr.HTML() | |
| video_info = gr.Markdown() | |
| with gr.Column(scale=2): | |
| length = gr.Number(label="Length of video") | |
| follower = gr.Number(label="Follower Count") | |
| total_likes = gr.Number(label="User Total Likes") | |
| total_video = gr.Number(label="User Total Videos") | |
| days_since = gr.Number(label="Days since the music first debut") | |
| third_row = gr.Row(variant='compact',equal_height=True, visible=False) | |
| with third_row: | |
| predict_button = gr.Button("Predict") | |
| prediction = gr.Number(label="Predicted Likes for the video",visible=False) | |
| submit_button.click(fn=visible_component, | |
| inputs=length, | |
| outputs=second_row).then(fn=get_video_details, | |
| inputs=video_url, | |
| outputs=[ | |
| tiktok_video, | |
| video_info, | |
| length, | |
| follower, | |
| total_likes, | |
| total_video, | |
| third_row | |
| ]) | |
| predict_button.click(fn=predict, | |
| inputs=[ | |
| length, | |
| follower, | |
| total_likes, | |
| total_video, | |
| days_since | |
| ], | |
| outputs=prediction).then(fn=visible_component, | |
| inputs=length, | |
| outputs=prediction) | |
| demo.launch() |