| import os |
| import gradio as gr |
| from gradio.components import ChatMessage |
| import openai |
| from pydantic import BaseModel |
| from langchain_openai import ChatOpenAI |
| from langchain_core.prompts import ChatPromptTemplate |
| from langchain_core.output_parsers import PydanticOutputParser |
| from langchain.agents import create_tool_calling_agent, AgentExecutor |
| from tools import save_tool |
|
|
| |
| if os.getenv("HF_SPACE", None) is None: |
| from dotenv import load_dotenv |
| load_dotenv() |
|
|
| |
| OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] |
| openai.api_key = OPENAI_API_KEY |
|
|
| |
| class ResearchResponse(BaseModel): |
| topic: str |
| empathetic_response: str |
| informative_response: str |
| quran: str |
| question: str |
| sources: list[str] |
| tools_used: list[str] |
|
|
| |
| llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY, model="gpt-4o-mini") |
| parser = PydanticOutputParser(pydantic_object=ResearchResponse) |
|
|
| |
| prompt = ChatPromptTemplate.from_messages([ |
| ( |
| "system", |
| """ |
| You are an empathetic, culturally and religiously sensitive AI assistant specializing in mental health support for Muslim women. Your responses must always: |
| |
| 1. Start with a kind acknowledgment of the user's feelings or situation. |
| 2. Offer a gentle validation grounded in shared experience or emotional context (e.g., "Many people feel this way" or "That makes sense given what you're going through"). |
| 3. Provide a concise, emotionally supportive response that includes both comforting reflection and practical suggestions, where appropriate. |
| 4. Provide a Quranic verse with a simple, relevant tafsir from Tafsir al-Mizan that directly supports the emotional or practical point being discussed. |
| 5. Ask a short, open-ended, growth-oriented reflection question that encourages deeper thought or emotional clarity. This question should be sensitive to Muslim women’s lived experiences and needs (e.g., family, community, faith, privacy, modesty). |
| |
| Optionally, if the topic is deep or complex, you may include a deeper analysis of the Quranic verse or tafsir snippet from Tafsir al-Mizan. However, only if it adds clarity, reassurance, or value to the user's experience. |
| |
| Use accessible, conversational language that feels warm and human. Avoid overly academic or robotic phrasing. |
| |
| Separate each sentence to be on a new line for readability. |
| |
| Always end the chat with the reflection question. |
| |
| After thinking, return a JSON matching the ResearchResponse schema. Don’t output any other text. |
| """, |
| ), |
| ("placeholder", "{chat_history}"), |
| ("human", "{query}"), |
| ("placeholder", "{agent_scratchpad}"), |
| ]) |
| prompt = prompt.partial(format_instructions=parser.get_format_instructions()) |
|
|
| |
| tools = [save_tool] |
| agent = create_tool_calling_agent(llm=llm, prompt=prompt, tools=tools) |
| agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=False) |
|
|
| |
|
|
| def respond(message, history: list[dict]): |
|
|
| agent_output = agent_executor.invoke({ |
| "query": message, |
| "chat_history": history |
| }) |
|
|
| raw = agent_output["output"] |
| print(message) |
| |
| try: |
| out = parser.parse(raw) |
| assistant_text = "\n".join([ |
| out.empathetic_response, |
| out.informative_response, |
| out.quran, |
| out.question, |
| ]) |
| except Exception as e: |
| print("Fallback to exception: raw") |
| assistant_text = raw |
|
|
| response = {"role": "assistant", "content": assistant_text} |
|
|
| yield response |
|
|
|
|
| |
| demo = gr.ChatInterface( |
| respond, |
| title="YAQIN Chatbot", |
| description="Culturally Sensitive Chatbot for Muslim Women Wanting Mental Healthcare. \n\n What\'s on your mind?", |
| type="messages", |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
