| import torch |
| from transformers import BertTokenizer, BertModel, GPT2LMHeadModel, GPT2Tokenizer |
| import numpy as np |
| import pandas as pd |
| import os |
| import gradio as gr |
|
|
| |
| bert_model_name = 'bert-base-uncased' |
| bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name) |
| bert_model = BertModel.from_pretrained(bert_model_name) |
|
|
| gpt2_model_name = 'gpt2' |
| gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name) |
| gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model_name) |
|
|
| |
| data = { |
| "questions": [ |
| "What is Rookus?", |
| "How does Rookus use AI in its designs?", |
| "What products does Rookus offer?", |
| "Can I see samples of Rookus' designs?", |
| "How can I join the waitlist for Rookus?", |
| "How does Rookus ensure the quality of its AI-generated designs?", |
| "Is there a custom design option available at Rookus?", |
| "How long does it take to receive a product from Rookus?" |
| ], |
| "answers": [ |
| "Rookus is a startup that leverages AI to create unique designs for various products such as clothes, posters, and different arts and crafts.", |
| "Rookus uses advanced AI algorithms to generate innovative and aesthetically pleasing designs. These AI models are trained on vast datasets of art and design to produce high-quality mockups.", |
| "Rookus offers a variety of products, including clothing, posters, and a range of arts and crafts items, all featuring AI-generated designs.", |
| "Yes, Rookus provides samples of its designs on its website. You can view a gallery of products showcasing the AI-generated artwork.", |
| "To join the waitlist for Rookus, visit our website and sign up with your email. You'll receive updates on our launch and exclusive early access opportunities.", |
| "Rookus ensures the quality of its AI-generated designs through rigorous testing and refinement. Each design goes through multiple review stages to ensure it meets our high standards.", |
| "Yes, Rookus offers custom design options. You can submit your preferences, and our AI will generate a design tailored to your specifications.", |
| "The delivery time for products from Rookus varies based on the product type and location. Typically, it takes 2-4 weeks for production and delivery." |
| ], |
| "default_answers": "I'm sorry, I cannot answer this right now. Your question has been saved, and we will get back to you with a response soon." |
| } |
|
|
| def get_bert_embeddings(texts): |
| inputs = bert_tokenizer(texts, return_tensors='pt', padding=True, truncation=True) |
| with torch.no_grad(): |
| outputs = bert_model(**inputs) |
| return outputs.last_hidden_state[:, 0, :].numpy() |
|
|
| def get_closest_question(user_query, questions, threshold=0.95): |
| all_texts = questions + [user_query] |
| embeddings = get_bert_embeddings(all_texts) |
| cosine_similarities = np.dot(embeddings[-1], embeddings[:-1].T) / ( |
| np.linalg.norm(embeddings[-1]) * np.linalg.norm(embeddings[:-1], axis=1) |
| ) |
| max_similarity = np.max(cosine_similarities) |
| |
| if max_similarity >= threshold: |
| most_similar_index = np.argmax(cosine_similarities) |
| return questions[most_similar_index], max_similarity |
| else: |
| return None, max_similarity |
|
|
| def generate_gpt2_response(prompt, model, tokenizer, max_length=100): |
| inputs = tokenizer.encode(prompt, return_tensors='pt') |
| outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1) |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
|
| def answer_query(user_query): |
| closest_question, similarity = get_closest_question(user_query, data['questions'], threshold=0.95) |
| if closest_question and similarity >= 0.95: |
| answer_index = data['questions'].index(closest_question) |
| answer = data['answers'][answer_index] |
| else: |
| excel_file = 'new_questions1.xlsx' |
| if not os.path.isfile(excel_file): |
| df = pd.DataFrame(columns=['question']) |
| df.to_excel(excel_file, index=False) |
| |
| new_data = pd.DataFrame({'questions': [user_query]}) |
| df = pd.read_excel(excel_file) |
| df = pd.concat([df, new_data], ignore_index=True) |
| with pd.ExcelWriter(excel_file, engine='openpyxl', mode='w') as writer: |
| df.to_excel(writer, index=False) |
| answer = data['default_answers'] |
|
|
| return answer |
|
|
| iface = gr.Interface( |
| fn=answer_query, |
| inputs="text", |
| outputs="text", |
| title="Rookus AI Query Interface", |
| description="Ask questions about Rookus and get answers generated by AI." |
| ) |
|
|
| if __name__ == "__main__": |
| iface.launch() |
|
|
