import gradio as gr
import openai

# OpenAI API Key (यहाँ अपनी API Key डालें)
openai.api_key = "YOUR_API_KEY"

# Backend Function: यूजर का मैसेज लेकर OpenAI से रिस्पॉन्स लाता है
def respond_to_message(message, chat_history):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": message}]
    )
    bot_message = response.choices[0].message['content']
    chat_history.append((message, bot_message))
    return "", chat_history

# Frontend: Gradio UI
with gr.Blocks() as demo:
    chatbot = gr.Chatbot(label="AI चैट बोर्ड")
    msg = gr.Textbox(label="आपका मैसेज")
    clear = gr.ClearButton([msg, chatbot])

    msg.submit(respond_to_message, [msg, chatbot], [msg, chatbot])

demo.launch()


from datasets import load_dataset

# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("KadamParth/NCERT_Chemistry_11th")

from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel

tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-community/openai-gpt")
model = TFOpenAIGPTModel.from_pretrained("openai-community/openai-gpt")

from transformers import OpenAIGPTTokenizer
import pandas as pd

# Dataset load karo (CSV, TXT, etc.)
df = pd.read_csv("your_dataset.csv")  # ya pd.read_table("your_dataset.txt", sep="\t")

# Tokenizer load karo (optional, agar similarity check karna hai)
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-community/openai-gpt")

# Question poochhein
question = "रासायनिक बंध क्या होता है?"

# Simple keyword-based search
def get_answer(question):
    for idx, row in df.iterrows():
        if question.lower() in row['question'].lower():
            return row['answer']
    return "जवाब नहीं मिला।"

answer = get_answer(question)
print(answer)