app.py

import gradio as gr
import os
import json
import requests
import xml.etree.ElementTree as ET

# OpenAI API URL and Key
API_URL = "https://api.openai.com/v1/chat/completions"
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

# Function to get product data from XML URL
def get_product_data(url):
    response = requests.get(url)
    root = ET.fromstring(response.content)
    products = []

    for item in root.findall('item'):
        if item.find('isOptionOfAProduct').text == '1':
            if int(item.find('stockAmount').text) > 0:
                name_words = item.find('rootlabel').text.lower().split()
                name = name_words[0]
                full_name = ' '.join(name_words)
                stockAmount = "stokta"
                price = item.find('priceWithTax').text
                item_info = (stockAmount, price)
                products.append((name, item_info, full_name))

    return products

# Load product data
url = 'https://www.alatin.com.tr/index.php?do=catalog/output&pCode=8582384479'
products = get_product_data(url)

def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {OPENAI_API_KEY}"
    }
    
    initial_message = [{"role": "user", "content": f"{inputs}"},]
    multi_turn_message = [
        {"role": "system", "content": "Bir önceki sohbeti unut. Vereceğin ürün bilgisi... (devamı)"}
    ]
    messages = multi_turn_message + initial_message

    input_words = [word.lower() for word in inputs.split()]

    for product_info in products:
        if product_info[0] in input_words:
            new_msg = f"{product_info[2]} {product_info[1][0]} ve fiyatı EURO {product_info[1][1]}"
            messages.append({"role": "system", "content": new_msg})
    
    for data in chatbot:
        messages.append({"role": "user", "content": data[0]})
        messages.append({"role": "assistant", "content": data[1]})
    
    messages.append({"role": "user", "content": inputs})
                
    payload = {
        "model": "gpt-4o",
        "messages": messages,
        "temperature": temperature,
        "top_p": top_p,
        "n": 1,
        "stream": True,
        "presence_penalty": 0,
        "frequency_penalty": 0,
    }

    response = requests.post(API_URL, headers=headers, json=payload, stream=True)

    history.append(inputs)
    token_counter = 0
    partial_words = ""

    for chunk in response.iter_lines():
        if chunk.decode():
            chunk = chunk.decode()
            if "content" in json.loads(chunk[6:])['choices'][0]['delta']:
                partial_words += json.loads(chunk[6:])['choices'][0]["delta"]["content"]
                if token_counter == 0:
                    history.append(" " + partial_words)
                else:
                    history[-1] = partial_words
                chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)]
                token_counter += 1
                yield chat, history, chat_counter

def reset_textbox():
    return gr.update(value='')

css = """
#chatbot {
    height: 450px;
    overflow: auto;
    display: flex;
    flex-direction: column-reverse;
}
.message {
    display: flex;
    padding: 8px;
    border-radius: 5px;
    margin: 5px 0;
    max-width: 60%;
}
.user {
    background-color: #DCF8C6;
    align-self: flex-end;
}
.assistant {
    background-color: #ECECEC;
    align-self: flex-start;
}
"""

theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="blue", text_size=gr.themes.sizes.text_sm)

with gr.Blocks(css=css, theme=theme) as demo:
    with gr.Column():
        chatbot = gr.Chatbot(label='Trek Asistanı', elem_id="chatbot")
        inputs = gr.Textbox(placeholder="Buraya yazın, yanıtlayalım.", show_label=False)
        state = gr.State([])
        top_p = gr.Slider(minimum=0, maximum=1.0, value=0.5, step=0.05, interactive=False, visible=False)
        temperature = gr.Slider(minimum=0, maximum=5.0, value=0.1, step=0.1, interactive=False, visible=False)
        chat_counter = gr.Number(value=0, visible=False, precision=0)

    inputs.submit(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter])
    inputs.submit(reset_textbox, [], [inputs])

demo.queue(max_size=10, concurrency_count=10).launch(debug=True)