| import gradio as gr |
| import openai |
|
|
| |
| openai.api_key = "YOUR_API_KEY" |
|
|
| |
| 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 |
|
|
| |
| 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 |
|
|
| |
| 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 |
|
|
| |
| df = pd.read_csv("your_dataset.csv") |
|
|
| |
| tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-community/openai-gpt") |
|
|
| |
| question = "रासायनिक बंध क्या होता है?" |
|
|
| |
| 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) |
|
|
