app.py

import gradio as gr
from rag_dspy import MedicalRAG

rag_chain = MedicalRAG()

sample_questions = [
    "What are the most common symptoms of lupus?",
    "How is type 2 diabetes usually managed in adults?",
    "What are the first-line medications for treating hypertension?",
]

def qa_bot(user_question, history, min_year, max_year, specialty):
    history = history or []
    if not user_question.strip():
        return history, history
    history.append({"role": "user", "content": user_question})
    result = rag_chain.forward(user_question, min_year, max_year, specialty)
    answer = result.final_answer
    history.append({"role": "assistant", "content": answer})
    return history, history

css = """
html, body, #root, .gradio-container {
    margin: 0 !important;
    padding: 0 !important;
}

body {
    background: #f5f7fa !important;
    font-family: 'Segoe UI', 'Roboto', 'Helvetica Neue', Arial, sans-serif !important;
    color: #212529 !important;
    margin: 0;
    padding: 0;
}
#main-row {
    display: flex;
    flex-direction: row;
    width: 100vw;
    min-height: 100vh;
    box-sizing: border-box;
    gap: 0;
}
#readme-col {
    flex: 1 1 0;
    background: #f5f7fa;
    padding: 0 0 0 0;
    border-right: 1px solid #e3e7ef;
    height: 108vh;
    overflow-y: auto;
    box-sizing: border-box;
}
#readme-markdown {
    border-radius: 5px;
    box-shadow: 0 4px 24px rgba(60, 80, 120, 0.07), 0 1.5px 4px rgba(60, 80, 120, 0.05);
    padding: 32px 32px 24px 32px;
    margin: 0;
    border: none;
    overflow-y: auto;
    box-sizing: border-box;
    flex: 1;
}
#main-card {
    background: #fff !important;
    border-radius: 5px;
    box-shadow: 0 4px 24px rgba(60, 80, 120, 0.07), 0 1.5px 4px rgba(60, 80, 120, 0.05);
    padding: 32px 32px 24px 32px;
    margin: 0;
    border: none;
    overflow-y: auto;
    box-sizing: border-box;
    flex: 1;
}
#chatbot {
    background: #f9fbfd !important;
    border-radius: 5px !important;
    box-shadow: 0 2px 8px rgba(60, 80, 120, 0.06);
    margin-bottom: 18px !important;
    border: 1px solid #e3e7ef !important;
    padding: 16px !important;
    color: #212529 !important;
}
.suggestion-btn {
    margin: 0 12px 18px 0 !important;
    background: #eaf4ff !important;
    color: #2563eb !important;
    border-radius: 10px !important;
    font-weight: 500 !important;
    font-size: 1rem !important;
    border: 1.5px solid #bcdfff !important;
    transition: background 0.2s, color 0.2s, border-color 0.2s;
}
.suggestion-btn:hover {
    background: #d2eaff !important;
    color: #174ea6 !important;
    border-color: #2563eb !important;
}
#medical-title {
    text-align: center;
    color: #2563eb;
    font-size: 2.3rem;
    font-weight: 700;
    margin-bottom: 18px;
    letter-spacing: 1px;
}
#user-input {
    border-radius: 10px !important;
    border: 1.5px solid #bcdfff !important;
    padding: 10px 16px !important;
    font-size: 1.1rem !important;
    background: #fff !important;
    color: #212529 !important;
}
#user-input::placeholder {
    color: #8ca0b3 !important;
    opacity: 1 !important;
}
#submit-btn {
    background: linear-gradient(90deg, #2563eb 0%, #38bdf8 100%) !important;
    color: #fff !important;
    border-radius: 10px !important;
    font-size: 1.3rem !important;
    min-width: 56px;
    min-height: 44px;
    border: none !important;
    box-shadow: 0 2px 8px rgba(60, 80, 120, 0.07);
    transition: background 0.2s;
}
#submit-btn:hover {
    background: linear-gradient(90deg, #174ea6 0%, #0ea5e9 100%) !important;
    color: #fff !important;
}
@media (max-width: 900px) {
    #main-row {
        flex-direction: column;
        width: 100vw;
    }
    #readme-col, #main-card {
        min-width: 0 !important;
        max-width: 100vw !important;
        border-radius: 0 !important;
        height: auto !important;
    }
    #readme-markdown {
        padding: 24px 12px 16px 12px;
    }
}
@media (max-width: 600px) {
    #main-row {
        flex-direction: column;
        width: 100vw;
    }
    #readme-col, #main-card {
        min-width: 0 !important;
        max-width: 100vw !important;
        border-radius: 0 !important;
        height: auto !important;
    }
    #readme-markdown {
        padding: 14px 6px 10px 6px;
        font-size: 0.98rem;
    }
    #main-card {
        padding: 16px 6px 10px 6px !important;
    }
}
"""

readme_content = """
#  Medical QA Chatbot

This is a Chain-of-Thought powered medical chatbot that:

- Retrieves answers from a Qdrant Cloud vector DB 
- Uses Stanford DSPy to reason step-by-step 
- Supports filtering by year and specialty
- Uses dense + ColBERT multivectors for retrieval

---

##  How to Use

- Add your `OPENAI_API_KEY` to `.env`
- Add your `QDRANT_API_KEY` to `.env`
- Add your `QDRANT_CLOUD_URL` to `.env`
- Make sure `qdrant-client` points to your
 Qdrant Cloud instance in `rag_dspy.py`
- Run `python app.py`

## Sample Questions 

### General Medical Knowledge
- What are the most common symptoms of lupus?

- How is type 2 diabetes usually managed in adults?

- What is the difference between viral and bacterial pneumonia?

### Treatment & Medication
- What are the first-line medications for treating hypertension?

- How does metformin work to lower blood sugar?

"""
def suggestion_click(q, history, min_year, max_year, specialty):
    return qa_bot(q, history, min_year, max_year, specialty)

with gr.Blocks(theme=gr.themes.Monochrome(), css=css) as demo:
    
    with gr.Row(elem_id="main-row"):
        with gr.Column(elem_id="main-card"):
            chatbot = gr.Chatbot(label="", elem_id="chatbot", type="messages", height=300)
            state = gr.State([])
            specialty_options = [
                "Rheumatology", "Psychiatry", "Pulmonology & Respiratory Medicine", "Nephrology", "Public Health & Epidemiology",
                "Medical Research & Methodology", "Pharmacy & Pharmacology", "Hematology", "Oncology", "Medical Ethics & Law",
                "Medical Technology & Informatics", "Infectious Disease", "Basic Medical Sciences", "Allergology", "Geriatrics",
                "Cardiology", "Gastroenterology & Hepatology", "General Surgery", "General Pediatrics", "Endocrinology & Metabolism",
                "Vascular Surgery", "Radiology & Imaging", "Obstetrics & Gynecology", "Orthopedic Surgery", "Neurology",
                "Family Medicine & Primary Care", "Psychology & Behavioral Health", "Otorhinolaryngology (ENT)", "General Internal Medicine",
                "Anesthesiology", "Physical & Rehabilitation Medicine", "Medical Education", "Healthcare Administration & Management",
                "Non-Medical Sciences & Disciplines", "Dermatology", "Critical Care & Intensive Care", "Urology", "Complementary & Alternative Medicine",
                "Cardiothoracic Surgery", "Neurosurgery", "Pediatric Subspecialties", "Occupational & Environmental Health", "Ophthalmology",
                "Emergency Medicine", "Dental & Oral Medicine", "Biomedical Engineering", "Pathology & Laboratory Medicine", "Transplant Surgery",
                "Preventive Medicine", "Genetics", "Nursing", "Allied Health Professions", "Plastic & Reconstructive Surgery", "Others",
                "Toxicology", "General Medicine"
            ]
            specialty_dropdown = gr.Dropdown(choices=specialty_options, value="General Medicine", label="Specialty", scale=2, elem_id="specialty-dropdown")
            with gr.Row():
                min_year_slider = gr.Slider(minimum=1793, maximum=2021, value=1990, step=1, label="Min Year", scale=2, elem_id="min-year-slider")
                max_year_slider = gr.Slider(minimum=1793, maximum=2021, value=2021, step=1, label="Max Year", scale=2, elem_id="max-year-slider")
            
            with gr.Row():
                user_input = gr.Textbox(placeholder="Type a medical question...", show_label=False, lines=1, scale=8, elem_id="user-input")
                submit_btn = gr.Button(value="➤", scale=1, elem_id="submit-btn")
            
            with gr.Row():
                suggestion_buttons = []
                for i, q in enumerate(sample_questions):
                    btn = gr.Button(q, elem_id=f"suggestion-{i}", elem_classes=["suggestion-btn"])
                    suggestion_buttons.append(btn)
                    
        submit_btn.click(qa_bot, inputs=[user_input, state, min_year_slider, max_year_slider, specialty_dropdown], outputs=[chatbot, state])
        user_input.submit(qa_bot, inputs=[user_input, state, min_year_slider, max_year_slider, specialty_dropdown], outputs=[chatbot, state])
        for btn, q in zip(suggestion_buttons, sample_questions):
            btn.click(suggestion_click, inputs=[gr.State(q), state, min_year_slider, max_year_slider, specialty_dropdown], outputs=[chatbot, state])
       
        with gr.Column(elem_id="readme-col"):
            gr.Markdown(readme_content, elem_id="readme-markdown")
            
            
            
            
if __name__ == "__main__":
    demo.launch()