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import gradio as gr |
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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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theme = gr.themes.Soft( |
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primary_hue="rose", |
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secondary_hue="zinc", |
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neutral_hue="pink" |
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) |
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custom_css = """ |
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:root { /* This applies to the light mode */ |
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--background-fill-primary: *primary_100 !important; /* Light pink */ |
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} |
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.dark { /* This applies to the dark mode */ |
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--background-fill-primary: #FFB6C1 !important; /* Hot pink */ |
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} |
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""" |
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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 women of all ages. You're friendly and caring. You give clear appropiate explainations with {info} and keep your explainations to 10 sentences maximum. You should make sure of the users age so you can give the most appropiate answer." |
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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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def show_info(topic): |
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responses = { |
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"General Health": 18009949662, |
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"Maternal Mental Health": 18338526262, |
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"Domestic Violence": 18007997233, |
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"Postpartum Support": 18009944773 |
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} |
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return responses.get(topic, "Select a topic to see more info.") |
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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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textbox= gr.Textbox(placeholder="Share Your Age and Ask Me Anything!"), |
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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 common symptoms of menopause?", |
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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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title_hotline= "# Select To Get Hotline Number" |
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with gr.Tabs(): |
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with gr.TabItem("Resources"): |
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gr.Markdown("### Resources") |
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open_google = gr.Button(value="🗓️ Period Tracker", link="https://drive.google.com/file/d/1_KNELAUDLLidwAT3fs2JBuO1yPgMGoDv/view") |
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open_google = gr.Button(value="👩🏻🍼 New Moms Support Group", link="https://www.instagram.com/firsttimemomsacademy/") |
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with gr.TabItem("Call a Hotline"): |
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gr.Markdown(title_hotline) |
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dropdown = gr.Dropdown(choices=["General Health", "Maternal Mental Health", "Domestic Violence", "Postpartum Support"], |
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label="Choose Your Hotline" |
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) |
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output = gr.Textbox(label="Hotline Info", interactive=False) |
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dropdown.change(fn=show_info, inputs=dropdown, outputs=output) |
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chatbot.launch(debug=True) |
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