Create app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+# --- CHANGE THIS ---
+YOUR_HF_USERNAME = "maliksahib1" 
+# -------------------
+
+# 1. Load the Model from the Hugging Face Hub
+# This will download our trained model file into the app's environment
+try:
+    model_path = hf_hub_download(
+        repo_id=f"{YOUR_HF_USERNAME}/tiktok-transparency-tool",
+        filename="tiktok_fake_follower_model.joblib"
+    )
+    model = joblib.load(model_path)
+except Exception as e:
+    # If the model fails to load, show an error message in the app
+    # This helps in debugging if the username or repo name is wrong
+    raise gr.Error(f"Failed to load the model. Please check your Hugging Face repo and username. Error: {e}")
+
+
+# 2. This is the main function that runs when the user clicks "Analyze"
+def analyze_profile(username):
+    if not username:
+        return "Please enter a TikTok username.", None
+
+    # --- THIS IS WHERE THE MAGIC HAPPENS ---
+    # In a real app, you would scrape TikTok for data here.
+    # For this demo, we use FAKE data to show how the model works.
+    # This ensures our app works without a complex scraper.
+    print(f"Analyzing {username}...")
+    
+    # MOCK DATA: A list of fake follower profiles to analyze
+    mock_follower_data = [
+        {'handle': '@user198237912', 'video_count': 0, 'follower_count': 5, 'following_count': 1500, 'has_bio': 0, 'is_generic_username': 1},
+        {'handle': '@user987654321', 'video_count': 0, 'follower_count': 2, 'following_count': 500, 'has_bio': 0, 'is_generic_username': 1},
+        {'handle': '@real_creator_from_demo', 'video_count': 50, 'follower_count': 15000, 'following_count': 200, 'has_bio': 1, 'is_generic_username': 0},
+        {'handle': '@another_creator_demo', 'video_count': 120, 'follower_count': 250000, 'following_count': 350, 'has_bio': 1, 'is_generic_username': 0},
+        {'handle': '@suspicious_but_not_generic', 'video_count': 1, 'follower_count': 12, 'following_count': 800, 'has_bio': 0, 'is_generic_username': 0},
+        {'handle': '@another_bot_account_123', 'video_count': 0, 'follower_count': 1, 'following_count': 100, 'has_bio': 0, 'is_generic_username': 0},
+    ]
+    
+    # Convert the fake data into a format our model understands
+    df = pd.DataFrame(mock_follower_data)
+    features = ['video_count', 'follower_count', 'following_count', 'has_bio', 'is_generic_username']
+    X_predict = df[features]
+    
+    # Use the model to predict the probability of being fake (1)
+    predictions_proba = model.predict_proba(X_predict)[:, 1]
+    df['suspicion_score'] = predictions_proba
+    
+    # 3. Generate a user-friendly report
+    total_followers_analyzed = len(df)
+    # We'll flag accounts with a suspicion score > 70%
+    suspicious_followers = df[df['suspicion_score'] > 0.70]
+    num_suspicious = len(suspicious_followers)
+    suspicion_rate = (num_suspicious / total_followers_analyzed) * 100
+    
+    report_text = f"""
+    ### Analysis Report for @{username}
+    - **Followers Analyzed:** {total_followers_analyzed} (Using sample data for this demo)
+    - **Highly Suspicious Followers Found:** {num_suspicious}
+    - **Overall Suspicion Rate:** **{suspicion_rate:.1f}%**
+    
+    *This analysis is for educational purposes and uses pre-loaded sample data, not live data from TikTok.*
+    """
+    
+    # Create a table to show the most suspicious accounts
+    output_df = suspicious_followers[['handle', 'suspicion_score']]
+    output_df = output_df.sort_values(by='suspicion_score', ascending=False).reset_index(drop=True)
+    
+    return report_text, output_df
+
+# 4. Create the Gradio web interface
+iface = gr.Interface(
+    fn=analyze_profile,
+    inputs=gr.Textbox(label="TikTok Username", placeholder="e.g., charlidamelio"),
+    outputs=[
+        gr.Markdown(label="Analysis Summary"),
+        gr.DataFrame(label="Top Suspicious Follower Accounts (from sample)")
+    ],
+    title="TikTok Transparency Tool (T3)",
+    description="Enter a TikTok username to analyze a sample of its followers. This tool uses a machine learning model to detect suspicious accounts based on public profile metrics.",
+    allow_flagging="never"
+)
+
+# Launch the app!
+iface.launch()