app.py

"""
HSAN1 Research Assistant - Final High Contrast Input Fix
"""
import os
import gradio as gr
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.messages import HumanMessage, SystemMessage

# Load environment variables
load_dotenv()

# --- 1. ENHANCED CSS FOR ACCESSIBILITY AND UI DESIGN ---
css = """
footer {display:none !important; visibility:hidden !important;}
.show-api {display:none !important;}
.built-with {display:none !important;}

/* 1. Background Outline */
.gradio-container {
    border: 3px solid #0056b3 !important; 
    border-radius: 15px;
    padding: 20px !important;
    background-color: #ffffff !important;
}

/* 2. Description Text */
.custom-description {
    font-size: 1.4rem !important;
    color: #000000 !important;
    font-weight: 700 !important;
    line-height: 1.4;
    margin-bottom: 20px;
}

/* 3. Assistant Responses */
.message-wrap .message, .prose p, .prose span, label {
    color: #000000 !important;
    font-weight: 700 !important;
    font-size: 1.1rem !important;
}

/* 4. FORCED HIGH CONTRAST PLACEHOLDER (The "Enter question here" text) */
/* We use multiple selectors to ensure it stays solid black and bold */
input::placeholder {
    color: #000000 !important;
    opacity: 1 !important; 
    font-weight: 900 !important;
}
::-webkit-input-placeholder {
    color: #000000 !important;
    opacity: 1 !important;
}
::-moz-placeholder {
    color: #000000 !important;
    opacity: 1 !important;
}
:-ms-input-placeholder {
    color: #000000 !important;
    opacity: 1 !important;
}

/* 5. Button Styling */
.gr-button-secondary {
    border: 2px solid #000000 !important;
    color: #000000 !important;
    font-weight: bold !important;
}
"""

# Configuration
INDEX_PATH = "./faiss_index"
SYSTEM_PROMPT = """You are a compassionate medical research assistant helping patients and families understand HSAN1. 
You have access to a database of 246 research documents including papers, newsletters, and family histories.
Instructions:
- Answer questions based ONLY on the provided context.
- If the answer is not in the context, say "I don't see that information in the research documents I have."
- Use clear, empathetic language and explain medical terms.
- Be accurate but hopeful in tone.
- Keep responses concise but informative."""

# Check for API key
api_key = os.environ.get("GOOGLE_API_KEY")
if not api_key:
    raise ValueError("GOOGLE_API_KEY environment variable not set")

# Load components
print("Loading embeddings model...")
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

print("Loading FAISS index...")
vectorstore = FAISS.load_local(INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

print("Initializing Gemini...")
llm = ChatGoogleGenerativeAI(
    model="gemini-3-flash-preview",
    temperature=0.3,
    streaming=True
)

def respond(message, history):
    docs = retriever.invoke(message)
    context = "\n\n---\n\n".join([doc.page_content for doc in docs])
    sources = list(set([os.path.basename(doc.metadata.get("source", "Unknown")) for doc in docs]))

    augmented_prompt = f"Context:\n{context}\n---\nUser question: {message}"
    messages = [SystemMessage(content=SYSTEM_PROMPT), HumanMessage(content=augmented_prompt)]

    response = ""
    for chunk in llm.stream(messages):
        if chunk.content:
            content = chunk.content
            if isinstance(content, list):
                content = "".join([i.get('text', '') if isinstance(i, dict) else i for i in content])
            response += content
            yield response

    if sources:
        yield response + f"\n\n---\n*Sources: {', '.join(sources[:3])}*"

# --- 2. INTERFACE SETUP ---
with gr.Blocks(css=css, title="HSAN1 Research Assistant") as demo:
    gr.Markdown("# 🧬 HSAN1 Research Assistant")
    
    # Instruction line
    gr.HTML("<p class='custom-description'>Scroll down to enter your question. Responses are based solely on contents of this website.</p>")
    
    gr.ChatInterface(
        respond,
        type="messages",
        theme="base",
        # Custom label for the Assistant response box
        chatbot=gr.Chatbot(label="The Assistant's response will appear in this box. This may take a moment or two.", show_label=True),
        # Custom placeholder for the input box
        textbox=gr.Textbox(placeholder="Enter question here", container=False, scale=7),
    )
    
    demo.footer_links = []

if __name__ == "__main__":
    demo.launch(show_api=False)