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import gradio as gr |
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import requests |
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import json |
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GEMINI_API_KEY = "AIzaSyAZ3WSpx_o53zpmhIJRzR0JMsiBOTnttbg" |
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API_URL = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={GEMINI_API_KEY}" |
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INTRO_TEXT = """ |
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<div> |
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<h1>氫鬆聊 - 專屬於您的健康諮詢夥伴 HydroChat - Your Personalized Assistant about Molecular Hydrogen </h1> |
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<p>本聊天機器人專為探討「氫分子 營養補充」搭配 <b>主流醫學</b> 、<b>臨床場景</b> 與 <b>健康保養</b> 的應用而設計。</p> |
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<p>我們致力於透過氫分子 營養補充，完成正規的臨床試驗、醫療期刊發表、查驗登記，來輔助主流醫學、幫助病人。</p> |
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</div> |
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""" |
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def call_gemini(message): |
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headers = {"Content-Type": "application/json"} |
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lang = detect(message) |
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if lang == "zh-cn" or lang == "zh-tw": |
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prompt = f"""你是「氫分子 營養補充食品」的諮詢機器人，專門協助使用者了解此類產品在醫學上的潛在應用。 |
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請根據以下指導原則提供建議與回應： |
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1. 鼓勵使用者思考如何將含氫分子食品與現有保健品或處方藥**搭配使用**，以期達成**協同作用**，但同時需提醒使用者就個別用藥與醫療人員確認。 |
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2. 在衛教或回答產品效果問題時，請**聚焦於氫分子（Molecular Hydrogen）的研究證據**與已知機制，包括抗氧化、抗發炎、細胞保護等面向。 |
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3. 回答必須**簡明、實證、不可編造**，避免誇大療效。 |
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情境：{message}""" |
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elif lang == "en": |
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prompt = f"""You are a scientific assistant chatbot specialized in hydrogen-rich coral calcium supplements. Please provide clinical guidance based on the following context (evidence-based, brief, no hallucinations): |
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Context: {message}""" |
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else: |
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prompt = f"""You are a chatbot specializing in molecular hydrogen supplements. Your purpose is to help users explore its potential health benefits based on scientific evidence. |
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Please follow these behavioral principles: |
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1. Encourage users to consider **synergistic use** of molecular hydrogen with their existing supplements or medications, while reminding them to consult with medical professionals. |
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2. When providing health education or answering about efficacy, focus specifically on **Molecular Hydrogen** and its **documented effects** such as antioxidation, anti-inflammation, and cellular protection. |
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3. Be **concise, evidence-based, and avoid speculative or exaggerated claims**. |
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Context: {message}""" |
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data = { |
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"contents": [ |
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{"parts": [{"text": prompt}]} |
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] |
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} |
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response = requests.post(API_URL, headers=headers, data=json.dumps(data)) |
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if response.status_code == 200: |
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try: |
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return response.json()['candidates'][0]['content']['parts'][0]['text'] |
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except Exception as e: |
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return f"[錯誤解析回應]: {str(e)}" |
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else: |
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return f"[API 錯誤 {response.status_code}]: {response.text}" |
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with gr.Blocks(title="氫鬆聊 - 專屬於您的健康諮詢夥伴") as demo: |
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gr.HTML(INTRO_TEXT) |
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chatbot = gr.Chatbot(height=400) |
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msg = gr.Textbox(label="請輸入您的問題", placeholder="例如：我正在接受＿＿治療，可以吃＿＿＿？") |
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with gr.Row(): |
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ask = gr.Button("提問") |
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clear = gr.Button("清除對話") |
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def respond(message, history): |
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reply = call_gemini(message) |
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history.append((message, reply)) |
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return "", history |
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msg.submit(respond, [msg, chatbot], [msg, chatbot]) |
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ask.click(respond, [msg, chatbot], [msg, chatbot]) |
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clear.click(lambda: [], None, chatbot) |
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demo.launch() |
