app.py

import pandas as pd
import gradio as gr
from sentence_transformers import SentenceTransformer, util

# Load merged influencer dataset
# Columns: Rank, Name, Followers, ER, Country, Niche, Reach, Source File, Source Path

df = pd.read_csv("top_100_influencers_combined.csv")
df.fillna("", inplace=True)

# Extract platform name from Source File (e.g., 'youtube_data_greece.csv' -> 'Youtube')
df['Platform'] = df['Source File'].str.split('_').str[0].str.capitalize()

# Prepare text for embedding (include platform)
profile_fields = ["Name", "Platform", "Niche", "Country"]
df["profile_text"] = df[profile_fields].agg(" - ".join, axis=1)

# Load embedding model
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
influencer_embeddings = model.encode(df["profile_text"].tolist(), convert_to_tensor=True)

# Recommendation logic: find top 3 by cosine similarity
def recommend_influencers(brand_description):
    query_embedding = model.encode(brand_description, convert_to_tensor=True)
    cosine_scores = util.pytorch_cos_sim(query_embedding, influencer_embeddings)[0]
    top_indices = cosine_scores.topk(3).indices.tolist()

    recs = []
    for idx in top_indices:
        row = df.iloc[idx]
        recs.append({
            "Name": row["Name"],
            "Platform": row.get("Platform", ""),
            "Niche": row["Niche"],
            "Country": row["Country"],
            "ER": row.get("ER", "N/A"),
            "Followers": row["Followers"],
            "Reach": row.get("Reach", "")
        })
    return recs

# Format recommendations into styled HTML cards
def format_output(brand_input):
    recs = recommend_influencers(brand_input)
    html = ""
    for i, rec in enumerate(recs, 1):
        html += f"""
        <div style='background:#ffffff; padding:1em; margin-bottom:1em; border-radius:8px; box-shadow:0 2px 6px rgba(0,0,0,0.1);'>
          <h3 style='margin:0; color:#0a1f44;'>🎯 {i}. {rec['Name']} <span style='font-size:0.9em; color:#555;'>({rec['Platform']})</span></h3>
          <p style='margin:0.5em 0;'><strong>Niche:</strong> {rec['Niche']}</p>
          <p style='margin:0.5em 0;'><strong>Country:</strong> {rec['Country']}</p>
          <p style='margin:0.5em 0;'><strong>Engagement:</strong> {rec['ER']}</p>
          <p style='margin:0.5em 0;'><strong>Followers:</strong> {rec['Followers']}</p>
          {f"<p style='margin:0.5em 0;'><strong>Reach:</strong> {rec['Reach']}</p>" if rec['Reach'] else ""}
        </div>
        """
    return html

# Build the Gradio interface
iface = gr.Interface(
    fn=format_output,
    inputs=gr.Textbox(
        lines=3,
        label="🗣️ Describe Your Campaign or Brand",
        placeholder="e.g., Targeted fitness brand outreach for Gen Z"
    ),
    outputs=gr.HTML(
        label="📈 Recommended Influencers"
    ),
    title="💡 Targeted Influencer Discovery for Social Media Marketing",
    description=(
        "Enhance your social media marketing by pinpointing the perfect influencers for your niche.\n\n"
        "🛠️ AI-driven matching based on niche, audience, and engagement metrics — get top 3 influencer recommendations instantly."
    ),
    article=(
        "**Project:** AI-Powered Influencer Recommender for Social Media Marketing\n"
        "**Model:** sentence-transformers/all-MiniLM-L6-v2 for semantic embeddings\n"
        "**Dataset:** Top 100 Social Media Influencers 2024 (Countrywise) from Kaggle"
    ),
    examples=[
        ["Sustainable fashion campaign targeting eco-conscious millennials"],
        ["Tech gadget launch aimed at early adopters in the US"],
        ["Healthy snack brand outreach for fitness enthusiasts"],
        ["Luxury travel experiences for affluent couples in Europe"]
    ],
    theme="soft",
    flagging_mode="never"
)

if __name__ == "__main__":
    iface.launch(share=True)