Deploy app with recommendation + slogan generator

app.py

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+
+import gradio as gr
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import faiss
+
+# === Load embedding model ===
+embed_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
+
+# Dummy dataset (for demo) – replace with your full startup dataset
+data = pd.DataFrame({
+    "name": ["HowDidIDo", "Museotainment", "Movitr"],
+    "tagline": ["Online evaluation platform", "PacMan & Louvre meet", "Crowdsourced video translation"],
+    "description": [
+        "Public speaking, Presentation skills and interview practice",
+        "Interactive AR museum tours",
+        "Video translation with voice and subtitles"
+    ]
+})
+
+# Build FAISS index
+data_vecs = embed_model.encode(data["description"].tolist())
+faiss.normalize_L2(data_vecs)
+index = faiss.IndexFlatIP(data_vecs.shape[1])
+index.add(data_vecs)
+
+def recommend(query, top_k=3):
+    query_vec = embed_model.encode([query])
+    faiss.normalize_L2(query_vec)
+    scores, idx = index.search(query_vec, top_k)
+    results = data.iloc[idx[0]].copy()
+    results["score"] = scores[0]
+    return results[["name", "tagline", "description", "score"]]
+
+def generate_slogan(description):
+    # Very simple slogan generator – can be replaced with FLAN-T5 later
+    return "Fresh Ideas for " + description.split()[0]
+
+def pipeline(user_input):
+    recs = recommend(user_input, top_k=3)
+    slogan = generate_slogan(user_input)
+    recs = recs.reset_index(drop=True)
+    recs.loc[len(recs)] = ["Generated Slogan", slogan, user_input, np.nan]
+    return recs
+
+examples = [
+    "AI coach for improving public speaking skills",
+    "Augmented reality app for interactive museum tours",
+    "Voice-controlled task manager for remote teams",
+    "Machine learning system for predicting crop yields",
+    "Platform for AI-assisted interior design suggestions"
+]
+
+demo = gr.Interface(
+    fn=pipeline,
+    inputs=gr.Textbox(label="Enter a startup description"),
+    outputs=gr.Dataframe(headers=["Name", "Tagline", "Description", "Score"]),
+    examples=examples,
+    title="SloganAI – Startup Recommendation & Slogan Generator",
+    description="Enter a startup idea and get top-3 similar startups + 1 generated slogan."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+transformers
+sentence-transformers
+faiss-cpu
+pandas
+numpy
+torch