Update app.py

app.py

@@ -1,6 +1,7 @@
-
+# app.py
 import os
 import random
+import re
 import pandas as pd
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
@@ -16,11 +17,11 @@ def create_synthetic_influencer_dataset(n=1200, out_csv="synthetic_influencers.c
     """
     Creates a synthetic dataset that mirrors your current schema:
     Columns: Rank, Name, Followers, ER, Country, Niche, Reach, Source File, Source Path
-    Uses a Hugging Face text-generation model to generate influencer names.
+    Uses a Hugging Face text-generation model to generate influencer names (simple catchy-username prompt).
     """
     random.seed(seed)
 
-    # Lazy import to keep dependencies minimal in environments where transformers isn't preinstalled
+    # Lazy import so the app still runs even if transformers isn't preinstalled locally
     try:
         from transformers import pipeline
     except Exception as e:
@@ -28,7 +29,7 @@ def create_synthetic_influencer_dataset(n=1200, out_csv="synthetic_influencers.c
             "Transformers not installed. Install with: pip install transformers torch"
         ) from e
 
-    # Use a lighter model to keep HF Space startup snappy (swap to 'gpt2' if you prefer)
+    # Small model for fast startup on Spaces; swap to "gpt2" if you prefer
     name_gen = pipeline("text-generation", model="distilgpt2")
 
     countries = [
@@ -43,12 +44,22 @@ def create_synthetic_influencer_dataset(n=1200, out_csv="synthetic_influencers.c
 
     rows = []
     for rank in range(1, n + 1):
-        # --- Name via HF text generation ---
-        prompt = "Short, catchy influencer name:"
-        raw = name_gen(prompt, max_new_tokens=8, num_return_sequences=1, do_sample=True, temperature=0.9)[0]["generated_text"]
+        # --- Name via HF text generation (simple catchy prompt) ---
+        prompt = "Short, catchy influencer username:"
+        out = name_gen(
+            prompt,
+            max_new_tokens=8,
+            num_return_sequences=1,
+            do_sample=True,
+            temperature=0.9,
+            top_p=0.92
+        )[0]["generated_text"]
+
         # cleanup
-        name = raw.replace(prompt, "").strip().split("\n")[0]
-        name = "".join(ch for ch in name if ch.isalnum() or ch in " -_.").strip()
+        name = out.replace(prompt, "").strip().split("\n")[0]
+        # keep letters/digits/space/dot/underscore/hyphen; then compress spaces
+        name = re.sub(r"[^A-Za-z0-9 _\.-]", "", name).strip()
+        name = name.replace(" ", "")
         if len(name) < 3 or len(name) > 40:
             name = f"Creator_{rank}"
 
@@ -87,7 +98,7 @@ def load_or_build_synthetic():
 df = load_or_build_synthetic()
 
 # =========================
-# FEATURE ENGINEERING (same as yours)
+# FEATURE ENGINEERING
 # =========================
 # Extract platform name from Source File (first token before '_'), capitalize
 df['Platform'] = df['Source File'].astype(str).str.split('_').str[0].str.capitalize()
@@ -97,7 +108,7 @@ profile_fields = ["Name", "Platform", "Niche", "Country"]
 df["profile_text"] = df[profile_fields].agg(" - ".join, axis=1)
 
 # =========================
-# EMBEDDINGS & RECOMMENDER (same as yours)
+# EMBEDDINGS & RECOMMENDER
 # =========================
 model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 influencer_embeddings = model.encode(df["profile_text"].tolist(), convert_to_tensor=True)
@@ -116,8 +127,8 @@ def recommend_influencers(brand_description):
             "Niche": row["Niche"],
             "Country": row["Country"],
             "ER": f"{row.get('ER', 'N/A')}",
-            "Followers": row["Followers"],
-            "Reach": row.get("Reach", "")
+            "Followers": int(row["Followers"]),
+            "Reach": int(row["Reach"]) if str(row.get("Reach", "")).isdigit() else row.get("Reach", "")
         })
     return recs
 
@@ -131,8 +142,8 @@ def format_output(brand_input):
           <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 ""}
+          <p style='margin:0.5em 0;'><strong>Followers:</strong> {rec['Followers']:,}</p>
+          {f"<p style='margin:0.5em 0;'><strong>Reach:</strong> {int(rec['Reach']):,}</p>" if isinstance(rec['Reach'], int) else ""}
         </div>
         """
     return html
@@ -156,9 +167,9 @@ iface = gr.Interface(
     article=(
         "**Project:** AI-Powered Influencer Recommender for Social Media Marketing\n\n"
         "**Models:**\n"
-        "- text-generation (Hugging Face) for synthetic influencer names (dataset creation)\n"
+        "- text-generation (Hugging Face) for synthetic influencer usernames (dataset creation)\n"
         "- sentence-transformers/all-MiniLM-L6-v2 for semantic embeddings (recommendations)\n\n"
-        "**Dataset:** 1,200-row synthetic influencer dataset generated at runtime."
+        "**Dataset:** 1,200-row synthetic influencer dataset generated at runtime (same schema as original)."
     ),
     examples=[
         ["Sustainable fashion campaign targeting eco-conscious millennials"],
@@ -172,4 +183,3 @@ iface = gr.Interface(
 
 if __name__ == "__main__":
     iface.launch(share=True)
-