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import gradio as gr |
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theme = gr.themes.Soft( |
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primary_hue="pink", |
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secondary_hue="pink", |
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neutral_hue="fuchsia", |
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).set( |
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link_text_color='*secondary_700' |
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) |
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from huggingface_hub import InferenceClient |
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from sentence_transformers import SentenceTransformer |
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import torch |
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import numpy as np |
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with open("knowledge.txt" , "r", encoding="utf-8") as f: |
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knowledge_base = f.read() |
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print("Knowledge base loaded.") |
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cleaned_text = knowledge_base.strip() |
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chunks = cleaned_text.split("\n") |
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cleaned_chunks = [] |
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for chunk in chunks: |
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stripped_chunk = chunk.strip() |
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if stripped_chunk: |
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cleaned_chunks.append(stripped_chunk) |
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print(cleaned_chunks) |
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model = SentenceTransformer('all-MiniLM-L6-v2') |
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chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True) |
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print(chunk_embeddings) |
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def get_top_chunks(query): |
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query_embedding = model.encode(query, convert_to_tensor=True) |
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query_embedding_normalized = query_embedding / query_embedding.norm() |
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chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True) |
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similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized) |
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print(similarities) |
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top_indices = torch.topk(similarities, k=3).indices |
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print(top_indices) |
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top_chunks = [] |
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for i in top_indices: |
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chunk = chunks[i] |
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top_chunks.append(chunk) |
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return top_chunks |
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client = InferenceClient("google/gemma-3-27b-it") |
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def respond(message,history): |
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info = get_top_chunks(message) |
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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." |
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}] |
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if history: |
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messages.extend(history) |
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messages.append({"role" : "user", "content" : message}) |
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response = "" |
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for message in client.chat_completion( |
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messages, |
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max_tokens = 500, |
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stream=True, |
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top_p = .2 |
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): |
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token = message.choices[0].delta.content |
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response += token |
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yield response |
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def display_image(): |
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return "Screenshot 2025-06-12 at 10.53.59 AM.png" |
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with gr.Blocks (theme = theme) as chatbot: |
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gr.Image(display_image()) |
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gr.ChatInterface(respond, type = "messages", |
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title = "Hi, I'm BloomBot! 🌸", |
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description = "This tool is here to listen and provide information on female health topics, and all discussions will be kept confidential. ❤️🩹", |
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examples = ["What are the different types of period products? ", |
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"What are some vitamins that are good for teenage girls?", |
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"What should I know about puberty?", |
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"Where can I find my nearest OBGYN?"] |
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) |
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with gr.Tab("Resources"): |
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gr.Markdown("### Resources") |
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gr.HTML(""" |
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<a href="https://drive.google.com/file/d/1_KNELAUDLLidwAT3fs2JBuO1yPgMGoDv/view" target="_blank"> |
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# <button style="font-size:16px;padding:10px 20px;margin-top:10px;"> |
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# 📄 Period Tracker |
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# </button> |
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</a> |
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# """) |
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chatbot.launch(debug=True) |
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