import gradio as gr from huggingface_hub import InferenceClient from sentence_transformers import SentenceTransformer import torch import numpy as np theme = gr.themes.Soft( primary_hue="rose", secondary_hue="zinc", neutral_hue="pink", ).set( link_text_color='*secondary_700', background_fill_primary = 'primary_600' ) custom_css = """ :root { /* This applies to the light mode */ --background-fill-primary: #FFB6C1 !important; /* Light pink */ } .dark { /* This applies to the dark mode */ --background-fill-primary: #FF69B4 !important; /* Hot pink */ } """ with open("knowledge.txt" , "r", encoding="utf-8") as f: knowledge_base = f.read() print("Knowledge base loaded.") cleaned_text = knowledge_base.strip() chunks = cleaned_text.split("\n") cleaned_chunks = [] for chunk in chunks: stripped_chunk = chunk.strip() if stripped_chunk: cleaned_chunks.append(stripped_chunk) print(cleaned_chunks) model = SentenceTransformer('all-MiniLM-L6-v2') chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True) print(chunk_embeddings) def get_top_chunks(query): query_embedding = model.encode(query, convert_to_tensor=True) 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) print(similarities) top_indices = torch.topk(similarities, k=3).indices print(top_indices) top_chunks = [] for i in top_indices: chunk = chunks[i] top_chunks.append(chunk) return top_chunks client = InferenceClient("google/gemma-3-27b-it") def respond(message,history): info = get_top_chunks(message) messages = [{"role": "system" , "content": f"Your name is BloomBot and you're a supportive and helpful chatbot catered towards teens ages 10-18. You give clear kid-appropiate explainations with {info} and keep your explainations to 10 sentences maximum." }] if history: messages.extend(history) messages.append({"role" : "user", "content" : message}) response = "" for message in client.chat_completion( messages, max_tokens = 500, stream=True, #temperature = .2 top_p = .2 ): token = message.choices[0].delta.content response += token # print(response) yield response #theme = gr.themes.Ocean( # primary_hue="pink", # secondary_hue="pink", # neutral_hue="fuchsia" # ) def display_image(): return "Screenshot 2025-06-12 at 10.53.59 AM.png" with gr.Blocks (theme = theme) as chatbot: # chatbot = gr.Chatbot() gr.Image(display_image()) gr.ChatInterface(respond, type = "messages", #theme = gr.themes.Soft( #primary_hue="pink", #secondary_hue="pink", #neutral_hue="fuchsia"), title = "Hi, I'm BloomBot! 🌸", textbox= gr.Textbox(placeholder="Share Your Age and Ask Me Anything!"), description = "This tool is here to listen and provide information on female health topics, and all discussions will be kept confidential. ❤️‍🩹", examples = ["What are the common symptoms of menopause?", "What are some vitamins that are good for teenage girls?", "What should I know about puberty?", "Where can I find my nearest OBGYN?"] ) title_hotline= "# Select your city to find the nearest hotline" hotline_text= """### Placeholder""" with gr.Tabs(): with gr.TabItem("Resources"): gr.Markdown("### Resources") gr.HTML(""" # # """) with gr.TabItem("Call a Hotline"): gr.Markdown(title_hotline) gr.Markdown(hotline_text) #with gr.Tab("Educational PDFs"): # gr.Markdown("### 📘 Helpful Resources") chatbot.launch(debug=True)