Update app.py

app.py

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+# 🔧 Install dependencies first (uncomment for local testing)
+# !pip install gradio pandas sentence-transformers
+
+import gradio as gr
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+
+# Load influencer dataset (replace path with uploaded HF dataset if needed)
+df = pd.read_csv("top_100_influencers.csv")  # <- upload to HF Space alongside this script
+
+# Fill NA just in case
+df.fillna("", inplace=True)
+
+# Combine fields for embedding
+df["profile_text"] = df["Name"] + " - " + df["Platform"] + " - " + df["Niche"] + " - " + df["Country"]
+
+# Load embedding model
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# Precompute embeddings
+influencer_embeddings = model.encode(df["profile_text"].tolist(), convert_to_tensor=True)
+
+# 🔍 Recommendation Function
+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()
+    
+    recommendations = []
+    for idx in top_indices:
+        row = df.iloc[idx]
+        recommendations.append({
+            "Influencer": row["Name"],
+            "Platform": row["Platform"],
+            "Niche": row["Niche"],
+            "Country": row["Country"],
+            "Engagement Rate": row.get("Engagement Rate", "N/A"),
+            "Followers": row.get("Followers", "N/A")
+        })
+    return recommendations
+
+# 🖼️ Gradio UI
+def format_output(brand_input):
+    recs = recommend_influencers(brand_input)
+    output = ""
+    for i, rec in enumerate(recs, 1):
+        output += f"### {i}. {rec['Influencer']} ({rec['Platform']})\n"
+        output += f"- Niche: {rec['Niche']}\n"
+        output += f"- Country: {rec['Country']}\n"
+        output += f"- Engagement Rate: {rec['Engagement Rate']}\n"
+        output += f"- Followers: {rec['Followers']}\n\n"
+    return output
+
+demo = gr.Interface(
+    fn=format_output,
+    inputs=gr.Textbox(label="Enter your brand description (e.g. 'Sustainable fashion for Gen Z')", placeholder="Describe your brand..."),
+    outputs=gr.Markdown(label="Top 3 Influencer Matches"),
+    title="InfluMatch: Influencer Recommender",
+    description="Describe your brand or campaign and get 3 matching influencer suggestions.",
+    examples=[
+        ["Tech gadgets for millennial men"],
+        ["Skincare brand for Gen Z in the US"],
+        ["Luxury travel experiences for couples"],
+        ["Eco-friendly fashion accessories"]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()