app.py

import gradio as gr
import faiss
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
import google.generativeai as genai
import re
import os

# Load data and FAISS index
def load_data_and_index():
    docs_df = pd.read_pickle("docs_with_embeddings (1).pkl")  # Adjust path for HF Spaces
    embeddings = np.array(docs_df['embeddings'].tolist(), dtype=np.float32)
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(embeddings)
    return docs_df, index

docs_df, index = load_data_and_index()

# Load SentenceTransformer
minilm = SentenceTransformer('all-MiniLM-L6-v2')

# Configure Gemini API using Hugging Face Secrets
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
if not GEMINI_API_KEY:
    raise ValueError("Gemini API key not found. Please set it in Hugging Face Spaces secrets.")
genai.configure(api_key=GEMINI_API_KEY)
model = genai.GenerativeModel('gemini-2.0-flash')

# Preprocess text function
def preprocess_text(text):
    text = text.lower()
    text = text.replace('\n', ' ').replace('\t', ' ')
    text = re.sub(r'[^\w\s.,;:>-]', ' ', text)
    text = ' '.join(text.split()).strip()
    return text

# Retrieve documents
def retrieve_docs(query, k=5):
    query_embedding = minilm.encode([query], show_progress_bar=False)[0].astype(np.float32)
    distances, indices = index.search(np.array([query_embedding]), k)
    retrieved_docs = docs_df.iloc[indices[0]][['label', 'text', 'source']]
    retrieved_docs['distance'] = distances[0]
    return retrieved_docs

# Parse response into structured sections
def parse_response(response_text):
    sections = {
        "Symptoms": [],
        "Signs": [],
        "Risk Factors": [],
        "Diagnostic Criteria": [],
        "Other": []
    }
    
    # Simple regex-based parsing (adjust based on your Gemini output format)
    lines = response_text.split('\n')
    current_section = "Other"
    
    for line in lines:
        line = line.strip()
        if line.lower().startswith("symptoms:"):
            current_section = "Symptoms"
        elif line.lower().startswith("signs:"):
            current_section = "Signs"
        elif line.lower().startswith("risk factors") or line.lower().startswith("past medical history:"):
            current_section = "Risk Factors"
        elif line.lower().startswith("diagnostic criteria:"):
            current_section = "Diagnostic Criteria"
        elif line and not line.startswith((' ', '\t')) and ':' in line:
            current_section = "Other"
        if line and not line.endswith(':'):
            sections[current_section].append(line)
    
    return sections

# Respond function with generic HTML formatting
def respond(message, system_message, max_tokens, temperature, top_p):
    # Preprocess the user message
    preprocessed_query = preprocess_text(message)
    
    # Retrieve relevant documents
    retrieved_docs = retrieve_docs(preprocessed_query, k=5)
    context = "\n".join(retrieved_docs['text'].tolist())
    
    # Construct the prompt with system message and RAG context
    prompt = f"{system_message}\n\n"
    prompt += (
        f"Query: {message}\n"
        f"Relevant Context: {context}\n"
        f"Generate a concise response to the query based only on the provided context. "
        f"Structure the response with clear sections like 'Symptoms:', 'Signs:', 'Risk Factors:', and 'Diagnostic Criteria:' where applicable."
    )
    
    # Generate response with Gemini
    response = model.generate_content(
        prompt,
        generation_config=genai.types.GenerationConfig(
            max_output_tokens=max_tokens,
            temperature=temperature
        )
    )
    answer = response.text.strip()

    # Parse the response into sections
    sections = parse_response(answer)

    # Format the response into HTML with CSS styling
    html_response = """
    <style>
        .diagnosis-container { font-family: Arial, sans-serif; line-height: 1.6; padding: 15px; max-width: 800px; margin: auto; }
        h2 { color: #2c3e50; font-size: 24px; margin-bottom: 15px; border-bottom: 2px solid #2980b9; padding-bottom: 5px; }
        h3 { color: #2980b9; font-size: 18px; margin-top: 15px; margin-bottom: 8px; }
        ul { margin: 0; padding-left: 25px; }
        li { margin-bottom: 6px; color: #34495e; }
        p { margin: 5px 0; color: #34495e; }
    </style>
    <div class="diagnosis-container">
        <h2>AI Response</h2>
        <p>Based on the provided context, here is the information relevant to your query:</p>
    """

    # Add sections dynamically
    for section, items in sections.items():
        if items:  # Only include sections that have content
            html_response += f"<h3>{section}</h3>"
            html_response += "<ul>"
            for item in items:
                # Remove section prefix if present (e.g., "Symptoms:" from the first line)
                cleaned_item = re.sub(rf"^{section}:", "", item, flags=re.IGNORECASE).strip()
                html_response += f"<li>{cleaned_item}</li>"
            html_response += "</ul>"

    html_response += "</div>"
    return html_response

# Simple Gradio Interface with HTML output
demo = gr.Interface(
    fn=respond,
    inputs=[
        gr.Textbox(label="Your Query", placeholder="Enter your medical question here (e.g., diabetes, heart failure)..."),
        gr.Textbox(
            value="You are a medical AI assistant diagnosing patients based on their query, using relevant context from past records of other patients.",
            label="System Message"
        ),
        gr.Slider(minimum=1, maximum=2048, value=150, step=1, label="Max New Tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.75, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",  # Included but not used by Gemini
        ),
    ],
    outputs=gr.HTML(label="Diagnosis"),
    title="🏥 Medical Query Assistant",
    description="A medical assistant that diagnoses patient queries using AI and past records, with styled output for any condition."
)

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