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| import os | |
| import pandas as pd | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| from llama_cpp import Llama # GGUF inference on CPU | |
| # ---------- model loading (done once at startup) ---------- | |
| MODEL_REPO = "TheBloke/phi-2-GGUF" # fully open 2.7 B model | |
| MODEL_FILE = "phi-2.Q4_K_M.gguf" # 4‑bit, 3.5 GB RAM | |
| CTX_SIZE = 2048 # ample for prompt+answer | |
| model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE) | |
| llm = Llama(model_path=model_path, | |
| n_ctx=CTX_SIZE, | |
| n_threads=os.cpu_count() or 2) # use all CPUs | |
| # ---------- analysis + generation ---------- | |
| def analyze_ads(file): | |
| df = pd.read_csv(file.name) | |
| req = {"headline","description","impressions","CTR","form_opens","spend"} | |
| if not req.issubset(df.columns): | |
| return f"Missing columns: {', '.join(req - set(df.columns))}" | |
| # numeric conversions | |
| for col in ["impressions","CTR","form_opens","spend"]: | |
| df[col] = pd.to_numeric(df[col], errors="coerce") | |
| df = df.dropna() | |
| df["engagement_rate"] = df["form_opens"] / df["impressions"] | |
| df["CPC"] = df["spend"] / (df["CTR"] * df["impressions"]).replace(0, pd.NA) | |
| df["cost_per_form_open"] = df["spend"] / df["form_opens"].replace(0, pd.NA) | |
| top = df.sort_values("CTR", ascending=False).head(3) | |
| worst = df.sort_values("CTR").head(3) | |
| def rows_to_text(sub): | |
| out = "" | |
| for _, r in sub.iterrows(): | |
| out += (f"Headline: {r.headline}\n" | |
| f"Description: {r.description}\n" | |
| f"Imp: {int(r.impressions)}, CTR: {r.CTR:.3f}, " | |
| f"Form Opens: {int(r.form_opens)}, ER: {r.engagement_rate:.3f}\n" | |
| f"Spend: ${r.spend:.2f}, CPC: ${r.CPC:.2f}, CPF: ${r.cost_per_form_open:.2f}\n\n") | |
| return out | |
| prompt = ( | |
| "You are a senior digital marketer.\n" | |
| "Analyse the high‑ and low‑performing ads below and deliver:\n" | |
| "1. Key patterns of winners.\n" | |
| "2. Weak points of losers.\n" | |
| "3. Three actionable creative improvements.\n\n" | |
| f"--- HIGH CTR ADS ---\n{rows_to_text(top)}" | |
| f"--- LOW CTR ADS ---\n{rows_to_text(worst)}" | |
| ) | |
| # generate (stream=False -> returns dict) | |
| answer = llm(prompt, max_tokens=320, temperature=0.7, top_p=0.9)["choices"][0]["text"] | |
| return answer.strip() | |
| # ---------- Gradio UI ---------- | |
| demo = gr.Interface( | |
| fn=analyze_ads, | |
| inputs=gr.File(label="CSV with: headline, description, impressions, CTR, form_opens, spend"), | |
| outputs=gr.Textbox(label="AI‑generated analysis & recommendations"), | |
| title="Ad Performance Analyzer (Phi‑2 4‑bit, CPU‑only)", | |
| description="Upload your ad data and get actionable insights without paid APIs." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |