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| import os | |
| import torch | |
| import pandas as pd | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| # Load model | |
| model_name = "google/flan-t5-small" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| # Load all product CSVs from nested directories | |
| def load_all_product_data(base_path="data"): | |
| all_data = [] | |
| for root, dirs, files in os.walk(base_path): | |
| for file in files: | |
| if file.endswith(".csv"): | |
| full_path = os.path.join(root, file) | |
| df = pd.read_csv(full_path) | |
| df["Brand"] = os.path.splitext(file)[0] | |
| df["Store"] = root.split(os.sep)[-4] # e.g., "Store C" | |
| df["Category"] = root.split(os.sep)[-2] | |
| all_data.append(df) | |
| return pd.concat(all_data, ignore_index=True) | |
| df = load_all_product_data("data") | |
| # Generate smart suggestion | |
| def generate_product_description(prompt): | |
| prompt = prompt.lower() | |
| # Basic price filter | |
| price_limit = 99999 | |
| if "under" in prompt: | |
| try: | |
| price_limit = int(prompt.split("under")[-1].split()[0]) | |
| except: | |
| pass | |
| filtered_df = df[df["Price"] <= price_limit] | |
| filtered_df = filtered_df[df["In Stock"].str.lower() == "yes"] | |
| if "dry hair" in prompt: | |
| filtered_df = filtered_df[filtered_df["Hair Type"].str.lower().str.contains("dry", na=False)] | |
| elif "oily hair" in prompt: | |
| filtered_df = filtered_df[filtered_df["Hair Type"].str.lower().str.contains("oily", na=False)] | |
| elif "normal hair" in prompt: | |
| filtered_df = filtered_df[filtered_df["Hair Type"].str.lower().str.contains("normal", na=False)] | |
| if "gift" in prompt: | |
| filtered_df = filtered_df[filtered_df["Tags"].str.contains("gift", case=False, na=False)] | |
| if "budget" in prompt: | |
| filtered_df = filtered_df[filtered_df["Tags"].str.contains("budget", case=False, na=False)] | |
| if filtered_df.empty: | |
| return "🤷 Sorry, no matching suggestions found." | |
| rows = [] | |
| for _, row in filtered_df.iterrows(): | |
| line = f"{row['Brand']} {row['Quantity']} – ₹{row['Price']} (Floor {row['Floor']}, Aisle {row['Aisle']})" | |
| if pd.notna(row.get("Offer")) and str(row["Offer"]).strip(): | |
| line += f"\n🎉 {row['Offer']}" | |
| rows.append(line) | |
| product_text = "\n".join(rows) | |
| model_prompt = f"Suggest top products:\n{product_text}" | |
| input_ids = tokenizer(model_prompt, return_tensors="pt").input_ids | |
| with torch.no_grad(): | |
| output_ids = model.generate(input_ids, max_new_tokens=100) | |
| response = tokenizer.decode(output_ids[0], skip_special_tokens=True) | |
| # Combine product suggestions and model response line by line | |
| return "\n\n".join(rows[:5]) + "\n\n🧠 AI Suggestion: " + response | |