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import gradio as gr |
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import requests |
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import json |
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import speech_recognition as sr |
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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>HydroChat – Your Personalized Assistant on Molecular Hydrogen Supplements</h1> |
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<p>This chatbot delivers evidence‑based guidance on using molecular hydrogen (H<sub>2</sub>) supplements in conjunction with conventional medicine and everyday health practices.</p> |
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<p>Our mission is to translate peer‑reviewed clinical research into practical advice so that users can make informed decisions about hydrogen‑rich coral‑calcium capsules.</p> |
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</div> |
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""" |
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def call_gemini(message: str) -> str: |
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"""Send the user message as a structured prompt to the Gemini endpoint and |
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return the assistant's reply text. Errors are surfaced in‑line for easier debugging. |
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""" |
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headers = {"Content-Type": "application/json"} |
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prompt = f"""You are HydroChat, an evidence‑based clinical assistant for an oral capsule product that contains\n- Coral calcium as a stable carrier of molecular hydrogen (H₂)\n- Additional vitamins and nutrients\n\nYour task is to provide scientifically sound health education and supplement‑drug interaction advice. Follow these principles:\n1. Encourage users to explore **synergistic use** of this hydrogen capsule with existing supplements or prescription medications, while reminding them to consult a qualified healthcare professional.\n2. Keep the discussion anchored on **molecular hydrogen**: its documented antioxidative, anti‑inflammatory, and cytoprotective mechanisms, and coral calcium's role as a solid‑state hydrogen carrier.\n3. All answers must be **concise, evidence‑based, and free of exaggerated or speculative claims**.\n4. Respond in the **same language** the user used.\n\nUser message: {message}\n""" |
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data = {"contents": [{"parts": [{"text": prompt}]}]} |
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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 (KeyError, IndexError) as err: |
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return f"[Parsing Error]: {err}" |
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else: |
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return f"[API Error {response.status_code}]: {response.text}" |
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def transcribe_audio(audio_file: str, lang_code: str) -> str: |
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"""Transcribe an uploaded audio file using Google Speech Recognition.""" |
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recognizer = sr.Recognizer() |
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with sr.AudioFile(audio_file) as source: |
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audio = recognizer.record(source) |
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try: |
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return recognizer.recognize_google(audio, language=lang_code) |
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except Exception as err: |
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return f"[Speech‑to‑Text Error]: {err}" |
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with gr.Blocks(title="HydroChat – Molecular Hydrogen Supplement Assistant") as demo: |
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gr.HTML(INTRO_TEXT) |
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chatbot = gr.Chatbot(height=400, label="HydroChat Conversation", show_copy_button=True) |
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with gr.Row(): |
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msg = gr.Textbox(label="Enter your question", placeholder="e.g. I am currently on __ therapy; can I take __?", scale=6) |
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lang_select = gr.Dropdown( |
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label="Speech recognition language", choices=[ |
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("中文 (台灣)", "zh-TW"), |
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("English (US)", "en-US"), |
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("日本語", "ja-JP"), |
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("한국어", "ko-KR"), |
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("Bahasa Indonesia", "id-ID"), |
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("Tiếng Việt", "vi-VN"), |
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("Français", "fr-FR"), |
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("Deutsch", "de-DE"), |
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], value="en-US", scale=2 |
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) |
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with gr.Row(): |
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audio_input = gr.Audio(label="🎙️ Record voice", type="filepath") |
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voice_to_text = gr.Button("Transcribe 🎤") |
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with gr.Row(): |
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ask = gr.Button("Submit") |
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clear = gr.Button("Clear Chat") |
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def respond(message: str, history: list): |
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if not message.strip(): |
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return "", history |
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reply = call_gemini(message) |
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history.append({"role": "user", "content": message}) |
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history.append({"role": "assistant", "content": reply}) |
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return "", history |
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def handle_audio(audio_path: str, lang_code: str): |
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if not audio_path: |
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return "" |
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return transcribe_audio(audio_path, lang_code) |
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msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot]) |
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ask.click(respond, inputs=[msg, chatbot], outputs=[msg, chatbot]) |
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clear.click(lambda: [], outputs=chatbot) |
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voice_to_text.click(handle_audio, inputs=[audio_input, lang_select], outputs=msg) |
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demo.launch() |
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