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| from sentence_transformers import SentenceTransformer | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import numpy as np | |
| import torch | |
| import os | |
| import gradio as gr | |
| #pip install https://gradio-builds.s3.amazonaws.com/75c684efb87624bee2fb63b08122564e6538509e/gradio-6.17.3-py3-none-any.whl | |
| def image_classifier(inp): | |
| return {'cat': 0.3, 'dog': 0.7} | |
| demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label") | |
| demo.launch() | |
| with open("knowledge.txt", "r", encoding="utf-8") as file: | |
| knowledge_base = file.read() | |
| def preprocess_text(text): | |
| cleaned_text = text.strip() | |
| chunks = cleaned_text.split("\n") | |
| cleaned_chunks = [] | |
| for chunk in chunks: | |
| stripped_chunk = chunk.strip() | |
| if len (stripped_chunk)>0: | |
| cleaned_chunks.append(stripped_chunk) | |
| #print(cleaned_chunks) | |
| #print (len(cleaned_chunks)) | |
| return cleaned_chunks | |
| cleaned_chunks = preprocess_text(knowledge_base) | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| def create_embeddings(text_chunks): | |
| chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True) # Replace ... with the text_chunks list | |
| #print(chunk_embeddings) | |
| #print(chunk_embeddings.shape) | |
| return chunk_embeddings | |
| chunk_embeddings = create_embeddings(cleaned_chunks)# Complete this line | |
| def get_top_chunks(query, chunk_embeddings, text_chunks): | |
| query_embedding = model.encode(query, convert_to_tensor=True) # Complete this line | |
| query_embedding_normalized = query_embedding / query_embedding.norm() | |
| chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True) | |
| similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized) # Complete this line | |
| #print(similarities) | |
| top_indices = torch.topk(similarities, k=3).indices | |
| #print(top_indices) | |
| top_chunks = [] | |
| for i in top_indices: | |
| chunk = text_chunks[i] | |
| top_chunks.append(chunk) | |
| return top_chunks | |
| top_results = get_top_chunks("Your account has been compromised", chunk_embeddings, cleaned_chunks) # Complete this line | |
| #print(top_results) | |
| #with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.pink)) as demo: | |
| cleaned_chunks = preprocess_text(knowledge_base) | |
| client = InferenceClient("Qwen/Qwen2.5-7B-Instruct", token=os.getenv("ByteShield_Token")) | |
| def respond(message, history): | |
| top_chunks = get_top_chunks(message, chunk_embeddings, cleaned_chunks) | |
| context = "\n".join(top_chunks) | |
| messages = [{"role": "system","content": f"You are a friendly, tech expert chatbot. Use this context to answer:\n{context}"}] | |
| if history: | |
| messages.extend(history) | |
| messages.append({"role": "user", "content": message}) | |
| response = client.chat_completion( | |
| messages, | |
| max_tokens=1000 | |
| ) | |
| return response.choices[0].message.content.strip() | |
| with gr.Blocks(theme=gr.themes.Ocean()) as demo: | |
| chatbot = gr.ChatInterface( | |
| fn = respond, | |
| cache_examples = False, | |
| textbox=gr.Textbox(placeholder="Ask me anything!", container=False, scale=7), | |
| title = "ByteShield - Your AI Gaurdian for Online Safety", | |
| description = "Ask me anything about online safety!", | |
| examples = ["Generate me some strong passwords to use.", "What are some security measures I can take to stay safe online?", "Explain how a data breach works.", "How do I know if a message is a scam or not?"] | |
| ) | |
| demo.launch() | |