Update app.py

app.py

@@ -2,69 +2,85 @@ import pandas as pd
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 
-# ✅ Load the CSV you uploaded into the Space
+# 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)
 
+# Prepare text for embedding
+# You can include 'Reach' or other metrics as needed
+profile_fields = ["Name", "Niche", "Country"]
+df["profile_text"] = df[profile_fields].agg(" - ".join, axis=1)
 
-# add platform?
-df["profile_text"] = df["Name"] + " - " + df["Niche"] + " - " + df["Country"] 
-
+# 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()
 
-    recommendations = []
+    recs = []
     for idx in top_indices:
         row = df.iloc[idx]
-        recommendations.append({
-            "Influencer": row["Name"],
+        recs.append({
+            "Name": row["Name"],
+            "Platform": row.get("Platform", ""),
             "Niche": row["Niche"],
             "Country": row["Country"],
-            "Engagement Rate": row.get("ER", "N/A"),
-            "Followers": row.get("Followers", "N/A")
+            "ER": row.get("ER", "N/A"),
+            "Followers": row["Followers"],
+            "Reach": row.get("Reach", "")
         })
-    return recommendations
+    return recs
+
+# Format recommendations into styled HTML cards
 
 def format_output(brand_input):
     recs = recommend_influencers(brand_input)
-    output = ""
+    html = ""
     for i, rec in enumerate(recs, 1):
-        output += f"### {i}. {rec['Name']})\n"
-        output += f"- Niche: {rec['Niche']}\n"
-        output += f"- Country: {rec['Country']}\n"
-        output += f"- Engagement Rate: {rec['ER']}\n"
-        output += f"- Followers: {rec['Followers']}\n\n"
-    return output
+        html += f"""
+        <div style='padding:1em; margin-bottom:1em; border:1px solid #e0e0e0; border-radius:8px; background:#fff;'>
+          <h3 style='margin:0 0 .5em;'>{i}. {rec['Name']} <span style='font-size:0.8em; color:#666;'>({rec['Platform']})</span></h3>
+          <p style='margin:0.2em 0;'><strong>Niche:</strong> {rec['Niche']}</p>
+          <p style='margin:0.2em 0;'><strong>Country:</strong> {rec['Country']}</p>
+          <p style='margin:0.2em 0;'><strong>Engagement Rate:</strong> {rec['ER']}</p>
+          <p style='margin:0.2em 0;'><strong>Followers:</strong> {rec['Followers']}</p>
+          {f"<p style='margin:0.2em 0;'><strong>Reach:</strong> {rec['Reach']}</p>" if rec['Reach'] else ""}
+        </div>
+        """
+    return html
 
-# Build the interface
+# Build the Gradio interface
 demo = gr.Interface(
     fn=format_output,
     inputs=gr.Textbox(
         label="Describe your brand or campaign",
         placeholder="e.g. Sustainable fashion brand targeting Gen Z in the US",
-        lines=3
+        lines=3,
+        elem_id="input-box"
     ),
-    outputs=gr.HTML(),
+    outputs=gr.HTML(elem_id="output-cards"),
     title="InfluMatch – AI-Powered Influencer Recommender",
     description="""
-    <div style='font-size: 1.1em; color: #333;'>
-        <p><strong>InfluMatch</strong> helps you discover the perfect influencers for your brand or campaign using state-of-the-art AI.</p>
-        <p>Just describe your brand, and we'll instantly recommend three top influencers who match your niche, audience, and goals.</p>
+    <div style='max-width:600px; margin-bottom:1em; color:#333;'>
+      <p><strong>InfluMatch</strong> helps you discover the ideal influencers for your brand using AI-driven similarity matching.</p>
+      <p>Simply enter a short description of your brand or campaign to get three top influencer recommendations.</p>
     </div>
     """,
+    theme="default",
     examples=[
-        ["Skincare brand for Gen Z women in the US"],
-        ["Luxury watches targeting high-net-worth individuals in Europe"],
-        ["Eco-friendly fitness gear for millennials"],
-        ["Pet food brand seeking TikTok creators in Canada"]
-    ],
-    theme="default"
+      ["Skincare brand for Gen Z women in the US"],
+      ["High-end travel gear targeting adventure enthusiasts in Europe"],
+      ["Eco-friendly fitness apparel for millennials"],
+      ["Gourmet pet food brand seeking Instagram influencers in Canada"]
+    ]
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)