import os
import uuid
import gradio as gr
import pandas as pd
import re
from groq import Groq
from amazon_apparel_recommender import price_quality_recommendations

# Load metadata
metadata = pd.read_csv("assets/cleaned_metadata.csv")
metadata['title'] = metadata['title'].astype(str)
metadata['color'] = metadata['color'].astype(str)
metadata['brand'] = metadata['brand'].astype(str)
metadata['product_type_name'] = metadata['product_type_name'].astype(str)

# Initialize Groq client
client = Groq(api_key=os.getenv("groqkey"))

# Initial system prompt
system_prompt = (
    "You are an Amazon fashion assistant. Users describe the kind of clothing they're looking for, and you recommend products based on metadata like brand, color, product type, and price. Keep responses short and clear."
)

# Flexible keyword-based filter (no need for price limit)
def filter_metadata(query):
    query = query.lower()
    keywords = re.findall(r'\w+', query)
    filtered = metadata.copy()

    for kw in keywords:
        if len(filtered) < 3:
            break
        filtered = filtered[
            filtered['title'].str.lower().str.contains(kw) |
            filtered['color'].str.lower().str.contains(kw) |
            filtered['brand'].str.lower().str.contains(kw) |
            filtered['product_type_name'].str.lower().str.contains(kw)
        ]

    return filtered[['title', 'brand', 'price', 'review_score']].head(3).to_dict(orient='records')

# Function to get Groq response
conversation_state = gr.State([])

def get_chat_response(query, history):
    if not history or history[0]["role"] != "system":
        history.insert(0, {"role": "system", "content": system_prompt})
    history.append({"role": "user", "content": query})

    # Add grounded product suggestions
    product_suggestions = filter_metadata(query)
    if product_suggestions:
        product_context = "\nHere are some matching products:\n"
        for p in product_suggestions:
            product_context += f"- {p['title']} by {p['brand']} (${p['price']}, score: {p['review_score']})\n"
    else:
        product_context = "\nSorry, I couldn't find matching items for that query.\n"

    history.append({"role": "assistant", "content": product_context})

    completion = client.chat.completions.create(
        model="deepseek-r1-distill-llama-70b",
        messages=history,
        temperature=0.4,
        top_p=0.95,
        stream=True
    )

    response = ""
    for chunk in completion:
        if chunk.choices[0].delta.content:
            response += chunk.choices[0].delta.content

    history.append({"role": "assistant", "content": response})

    # Render conversation pairs explicitly
    chat_display = []
    for i in range(len(history)):
        if history[i]["role"] == "user":
            user_msg = history[i]["content"]
            assistant_msg = history[i + 1]["content"] if i + 1 < len(history) and history[i + 1]["role"] == "assistant" else "(no response)"
            chat_display.append((user_msg, assistant_msg))

    return history, chat_display, ""

def clear_chat():
    return [], [], ""

with gr.Blocks(title="🛍️ Amazon Chat Recommender") as demo:
    gr.HTML("""
        <h2 style='text-align: center;'>🛍️ Amazon Apparel Recommender (Groq Chat)</h2>
        <p style='text-align: center;'>Ask for clothing recommendations and get chat-based responses.</p>
    """)

    chatbot = gr.Chatbot(label="🧵 Apparel Chat", min_height=500)
    user_query = gr.Textbox(label="Ask for a recommendation", placeholder="e.g. black hoodie or summer dress")
    submit = gr.Button("Send")
    clear = gr.Button("Clear")

    demo_state = gr.State([])

    submit.click(fn=get_chat_response, inputs=[user_query, demo_state], outputs=[demo_state, chatbot, user_query])
    user_query.submit(fn=get_chat_response, inputs=[user_query, demo_state], outputs=[demo_state, chatbot, user_query])
    clear.click(fn=clear_chat, outputs=[demo_state, chatbot, user_query])

    demo.launch()