| import gradio as gr |
| from huggingface_hub import InferenceClient |
| from sentence_transformers import SentenceTransformer |
| import torch |
| import numpy as np |
|
|
| 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): |
| if "nearest obgyn" in message.lower(): |
| |
| return "Sure! Please click on the 'OBGYN Finder' tab above to get started. 🩺", history |
| |
| messages = [{"role": "system" , "content": "Your name is BloomBot and you're a supportive and helpful chatbot catered towards teens ages 10-18. You give clear kid-appropiate explainations 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, |
| |
| top_p = .2 |
| ): |
| token = message.choices[0].delta.content |
| response += token |
| yield response |
| |
| print(response) |
| |
| theme = gr.themes.Ocean( |
| primary_hue="pink", |
| secondary_hue="pink", |
| neutral_hue="fuchsia" |
| ) |
|
|
| def display_image(): |
| return "BannerBot.jpg" |
|
|
| with gr.Blocks (theme=theme) as chatbot: |
| gr.Image(display_image()) |
| gr.ChatInterface(respond, type = "messages", |
| |
| |
| |
| title = "Hi, I'm BloomBot!", |
| examples = ["What are the different types of period products? ", |
| "What are some vitamins that are good for teenage girls?", |
| "What should I know about puberty?", |
| "Where can I find my nearest OBGYN?"] |
| ) |
| with gr.Tab("OBGYN Finder") as obgyn_tab: |
| gr.Markdown("### Find a Nearby OBGYN") |
| location_dropdown = gr.Dropdown( |
| choices=["New York", "California", "Texas", "Florida"], |
| label="Choose your state" |
| ) |
| result_text = gr.Textbox(label="Nearby Clinics") |
| location_dropdown.change(fn=show_obgyn_info, inputs=location_dropdown, outputs=result_text) |
| |
| |
| with gr.Tab("Resources"): |
| gr.Markdown("### Resources") |
| gr.HTML(""" |
| <a href="https://drive.google.com/file/d/1_KNELAUDLLidwAT3fs2JBuO1yPgMGoDv/view" target="_blank"> |
| <button style="font-size:16px;padding:10px 20px;margin-top:10px;"> |
| 📄 Period Tracker |
| </button> |
| </a> |
| """) |
|
|
| |
| |
| |
|
|
| chatbot.launch(debug=True) |
|
|
|
|
