# -*- coding: utf-8 -*-
"""get_brand_name.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1xmza5DAj2s5uh_SNFAIGOGPOPaBFIIKt

# ChatGPT API



"""## Part 1: Identify Brand Name"""

import openai
import pandas as pd
import gradio as gr
import os
import tempfile

# Replace with your OpenAI API key (from environment variable)
openai.api_key = os.getenv("OPENAI_API_KEY")

prompt = "Identify the brand name from a list of product or mixed product-brand names I provide. The brand name could be pure Traditional Chinese or pure English or mix. If no brand name exists in a given entry, respond with no_brand. If you think the title is the brand name, it is ok to provide the same name as title."

# Function to call the GPT model
def call_gpt_4o_mini(prompt: str, input_text: str, temperature=0.7, top_p=0.5):
    try:
        response = openai.ChatCompletion.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": prompt},
                {"role": "user", "content": input_text}
            ],
            max_tokens=20000,  # Adjust based on the model's token limit
            temperature=temperature,
        )
        return response.choices[0].message['content'] if response else "No response from model"
    except Exception as e:
        return f"Error: {str(e)}"

# Function to identify brands in a text
def identify_brands(text):
    return call_gpt_4o_mini(prompt, text)

# Function to process the uploaded file and identify brand names
def process_file(file):
    # Detect file type based on extension and read accordingly
    if file.name.endswith(".csv"):
        df = pd.read_csv(file.name, header=None, names=["Product Title"])
    elif file.name.endswith(".txt"):
        with open(file.name, "r", encoding="utf-8") as f:
            lines = f.readlines()
        df = pd.DataFrame({"Product Title": [line.strip() for line in lines if line.strip()]})
    else:
        raise ValueError("Unsupported file format. Please upload a CSV or TXT file.")

    # Apply the brand identification function to each product title
    df['Identified Brand'] = df['Product Title'].apply(identify_brands)
    return df

#  Gradio interface function
def process_inputs(input_text, file):
    if input_text and not file:
        # Process text input
        return identify_brands(input_text), None, None
    elif file and not input_text:
        try:
            result_df = process_file(file)

            # Save the DataFrame to a temporary CSV file
            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
            result_df.to_csv(temp_file.name, index=False)
            return None, result_df, temp_file.name
        except Exception as e:
            return f"Error: {str(e)}", None, None
    else:
        return "Please provide either a product title or upload a file, not both.", None, None

# Define the Gradio interface
interface = gr.Interface(
    fn=process_inputs,
    inputs=[
        gr.Textbox(label="Enter Product Title", placeholder="Type product title here...", lines=1),
        gr.File(label="Upload CSV/TXT File")
    ],
    outputs=[
        gr.Textbox(label="Identified Brand"),
        gr.Dataframe(label="Identified Brands from File"),
        gr.File(label="Download CSV")
    ],
    title="Brand Name Identifier",
    description="Enter a product title to identify the brand or upload a CSV/TXT file containing product titles."
)

# Launch the interface
interface.launch()