app.py

import os
import openai
import gradio as gr
from datetime import datetime

# ------------------------------------------------------------
# App Identity
# ------------------------------------------------------------
APP_TITLE = "Parampara & Prayog — Hindi Sahitya Adhyayan & Anusandhān Kendra"
APP_DESCRIPTION = (
    "A digital mentorship and research space inspired by the academic legacy "
    "of Presidency University’s Faculty of Arts — bringing together Parampara (Tradition) "
    "and Prayog (Experiment) in Hindi Studies."
)

# ------------------------------------------------------------
# OpenAI API Key Setup
# ------------------------------------------------------------
openai.api_key = os.getenv("OPENAI_API_KEY")

# ------------------------------------------------------------
# Professor Voice Prompt
# ------------------------------------------------------------
BASE_PROMPT = """
You are a senior Professor of Hindi Literature and former Dean of Arts at Presidency University, Kolkata.
You specialise in Modern & Contemporary Hindi Poetry, Comparative Literature, Religion & Society in Hindi Texts,
and literary research guidance.

Write in a calm, reflective, academic tone.
When you answer:
- Give conceptual explanations, not bullet points.
- Quote Hindi authors or critics where appropriate.
- Encourage comparative analysis.
- Use Devanagari for quotes and titles; English for explanation unless full Hindi mode is selected.
"""

# ------------------------------------------------------------
# Dropdown Options
# ------------------------------------------------------------
DOMAINS = [
    "Modern Hindi Poetry (आधुनिक हिंदी कविता)",
    "Contemporary Hindi Poetry (समकालीन कविता)",
    "Medieval Hindi Literature (मध्यकालीन हिंदी साहित्य)",
    "Modern Hindi Fiction & Prose (आधुनिक गद्य)",
    "Comparative Hindi Literature (तुलनात्मक साहित्य)",
    "Religion & Philosophy in Hindi Literature (धर्म और दर्शन)",
    "Research Methodology in Hindi Studies (अनुसंधान पद्धति)",
    "Translation Studies (अनुवाद अध्ययन)",
    "Dalit & Feminist Literature (दलित और नारीवादी साहित्य)",
    "Postmodern and Global Hindi (उत्तर आधुनिकता और वैश्विक हिंदी)",
]

AUDIENCES = [
    "Undergraduate Student",
    "Postgraduate Student",
    "PhD / Research Scholar",
    "Faculty / Teacher",
    "Independent Reader",
]

# ------------------------------------------------------------
# LLM Response
# ------------------------------------------------------------
def generate_response(domain, query, audience, language):
    lang_note = (
        "Respond in English with Devanagari quotes."
        if language == "English"
        else "Respond fully in Hindi (Devanagari)."
    )
    prompt = (
        f"{BASE_PROMPT}\n\nDomain: {domain}\nAudience: {audience}\n{lang_note}\n\n"
        f"Student Query:\n{query}\n\nAnswer:"
    )
    try:
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "system", "content": prompt}],
            temperature=0.7,
            max_tokens=800,
        )
        return response["choices"][0]["message"]["content"]
    except Exception as e:
        return f"⚠️ Error fetching response: {e}"

# ------------------------------------------------------------
# Knowledge Base (Example Notes)
# ------------------------------------------------------------
KNOWLEDGE_BASE = {
    "Research Guidance": [
        "How to select a topic for Hindi PhD.",
        "Common pitfalls in literary analysis.",
        "Structuring dissertation chapters.",
        "Citation and referencing in Hindi research.",
    ],
    "Comparative Literature": [
        "Hindi–Bengali modernist influences.",
        "Urdu–Hindi poetic dialogue.",
        "Translation as interpretation.",
        "Cross-cultural motifs in Hindi fiction.",
    ],
    "Commentaries": [
        "Annotated readings of Agyeya, Nirala, and Muktibodh.",
        "Bhakti and Modernity: Kabir to Nagarjun.",
        "Gender in Hindi poetry: Mahadevi Verma and beyond.",
    ],
}

# ------------------------------------------------------------
# Interface Logic
# ------------------------------------------------------------
def mentorship_interface(domain, audience, query, language):
    return generate_response(domain, query, audience, language)

# ------------------------------------------------------------
# Gradio Interface
# ------------------------------------------------------------
with gr.Blocks(title=APP_TITLE) as demo:
    gr.Markdown(f"## 🌸 {APP_TITLE}")
    gr.Markdown(APP_DESCRIPTION)

    with gr.Tab("A. Study Modules & Guidance"):
        domain = gr.Dropdown(choices=DOMAINS, label="Select Literary Domain")
        audience = gr.Dropdown(choices=AUDIENCES, label="Audience Type")
        query = gr.Textbox(label="Enter your academic query", lines=4)
        language = gr.Radio(["English", "Hindi"], label="Response Language", value="English")
        output = gr.Textbox(label="Professor’s Response", lines=12)
        ask_btn = gr.Button("Ask for Guidance")
        ask_btn.click(mentorship_interface, [domain, audience, query, language], output)

    with gr.Tab("B. Research Mentorship"):
        gr.Markdown("💡 Detailed mentoring on research design and methodology:")
        for topic in KNOWLEDGE_BASE["Research Guidance"]:
            gr.Markdown(f"- {topic}")

    with gr.Tab("C. Comparative Literature Lab"):
        gr.Markdown("🔶 Intersections of Hindi with other literatures:")
        for topic in KNOWLEDGE_BASE["Comparative Literature"]:
            gr.Markdown(f"- {topic}")

    with gr.Tab("D. Annotated Texts & Commentaries"):
        gr.Markdown("📘 Selected commentaries and readings:")
        for topic in KNOWLEDGE_BASE["Commentaries"]:
            gr.Markdown(f"- {topic}")

    with gr.Tab("E. Recorded Intellectual Presence"):
        gr.Markdown("🎙️ Placeholder for lectures and reflections.")
        gr.Markdown("_Coming soon: Audio-visual archives of the Professor._")

    with gr.Tab("F. Ask the Professor"):
        gr.Markdown("✍️ Submit questions for asynchronous academic discussion.")
        question = gr.Textbox(label="Your Question", lines=3)
        reply = gr.Textbox(label="Response", lines=8)
        submit_btn = gr.Button("Submit Question")
        submit_btn.click(mentorship_interface, [domain, audience, question, language], reply)

    with gr.Tab("Audience & Contribution"):
        gr.Markdown(
            "👥 Former students and scholars may contribute annotated notes, "
            "comparative findings, and new research topics."
        )

demo.launch(server_name="0.0.0.0", server_port=7860, share=True)