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import gradio as gr
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from sentence_transformers import SentenceTransformer
import faiss
import torch
# -------------------------------
# Load dataset
# -------------------------------
file_path = "marketing-campaigns.csv"
df = pd.read_csv(file_path)
# Combine text for embeddings
df = df.dropna(subset=["campaign_name", "description"])
df["text"] = df["campaign_name"] + ": " + df["description"]
# -------------------------------
# Embeddings + FAISS index
# -------------------------------
embed_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = embed_model.encode(df["text"].tolist(), convert_to_tensor=True, show_progress_bar=True)
embeddings_np = embeddings.detach().cpu().numpy()
# Build FAISS index
d = embeddings_np.shape[1]
index = faiss.IndexFlatL2(d)
index.add(embeddings_np)
# -------------------------------
# Load LLM (Phi-4-mini)
# -------------------------------
model_name = "microsoft/phi-4-mini"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32, device_map="auto")
# -------------------------------
# RAG functions
# -------------------------------
def retrieve_context(query, k=3):
query_vec = embed_model.encode([query], convert_to_tensor=True).cpu().numpy()
D, I = index.search(query_vec, k)
results = [df.iloc[i]["text"] for i in I[0]]
return results
def generate_with_rag(prompt):
# Step 1: Retrieve top campaigns
context = retrieve_context(prompt, k=3)
context_str = "\n".join(context)
# Step 2: Build final prompt
rag_prompt = f"""
You are an AI marketing assistant.
Here are some past campaigns for reference:\n{context_str}\n
Based on these, generate a new campaign idea for: {prompt}
"""
# Step 3: Generate with Phi-4
inputs = tokenizer(rag_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=200, temperature=0.7, top_p=0.9)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# -------------------------------
# Gradio UI
# -------------------------------
with gr.Blocks() as demo:
gr.Markdown("## 🤖 RAG-powered AI Marketing Campaign Generator")
with gr.Row():
query = gr.Textbox(label="Enter campaign idea or analysis query")
output = gr.Textbox(label="Generated Campaign")
btn = gr.Button("Generate with RAG")
btn.click(generate_with_rag, inputs=query, outputs=output)
if __name__ == "__main__":
demo.launch() |