Update app.py

app.py

@@ -1,181 +1,171 @@
-"""
-ARMYINTEL 5D-MDMP INTELLIGENCE ANALYSIS ENGINE
-Purpose: Comprehensive multi-discipline intelligence synthesis using 5D & MDMP
-Domain: Military Intelligence Training / Defensive Operations
-Frameworks: 5D (Detect, Deter, Deny, Deliver, Destroy) + MDMP
-"""
-
 import os
-import json
-from datetime import datetime
-from docx import Document
-from docx.shared import Pt
-from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
-import gradio as gr
 import openai
-import concurrent.futures
-
-# -------------------------
-# CONFIG
-# -------------------------
-OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
-if not OPENAI_API_KEY:
-    raise ValueError("OpenAI API Key not found in environment secrets.")
-
-openai.api_key = OPENAI_API_KEY
-
-MILITARY_INTEL_TECHNIQUES = [
-    "HUMINT", "SIGINT", "IMINT", "OSINT", "MASINT",
-    "COMINT", "ELINT", "TECHINT", "FINT", "SOCMINT",
-    "GEOINT", "ALL-SOURCE INTELLIGENCE", "CRISIS INTELLIGENCE",
-    "COUNTERINTELLIGENCE", "RED TEAMING", "BLUE TEAMING",
-    "CTRIP Analysis", "SALUT Analysis", "Decision Trees",
-    "Bayesian Reasoning", "Network Influence Mapping"
-]
+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."
+)
 
-# -------------------------
-# CORE 5D FUNCTION
-# -------------------------
-def apply_5d_to_technique(technique_name, problem_text):
-    prompt = f"""
-Conduct a professional military intelligence analysis for the following operational or training situation:
-Technique: {technique_name}
-Apply the 5D framework (Detect, Deter, Deny, Deliver, Destroy) and give structured findings.
-Include likely enemy capabilities, indicators, vulnerabilities, and recommended defensive measures.
-Problem: {problem_text}
+# ------------------------------------------------------------
+# API Key
+# ------------------------------------------------------------
+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 scholarly, reflective tone.
+When answering:
+- Give conceptual explanations rather than bullet points.
+- Quote relevant Hindi authors or critics where appropriate.
+- Encourage comparative and analytical thinking.
+- Use Devanagari for titles/quotes, English for discussion unless full-Hindi mode is chosen.
 """
+
+# ------------------------------------------------------------
+# Domain & Audience Dropdowns
+# ------------------------------------------------------------
+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 Function
+# ------------------------------------------------------------
+def generate_response(domain, query, audience, language):
+    lang_note = (
+        "Respond in English with Devanagari quotes."
+        if language == "English"
+        else "Respond entirely in Hindi (Devanagari script)."
+    )
+    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-4-turbo",
-            messages=[
-                {"role": "system", "content": "You are a senior army intelligence officer producing analytical reports for training and defensive planning."},
-                {"role": "user", "content": prompt}
-            ],
-            temperature=0.5,
-            max_tokens=600
+        resp = openai.ChatCompletion.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "system", "content": prompt}],
+            temperature=0.7,
+            max_tokens=900,
         )
-        text = response['choices'][0]['message']['content'].strip()
-        try:
-            data = json.loads(text)
-        except:
-            data = {
-                "technique": technique_name,
-                "findings": text,
-                "5D": {},
-                "recommended_actions": [],
-                "confidence": {"level":"Unknown","reason":"Model returned narrative text"}
-            }
-        data.setdefault("technique", technique_name)
-        data.setdefault("findings", "")
-        data.setdefault("5D", {})
-        data.setdefault("recommended_actions", [])
-        data.setdefault("confidence", {"level":"Unknown","reason":"Missing"})
-        return data
+        return resp["choices"][0]["message"]["content"]
     except Exception as e:
-        return {
-            "technique": technique_name,
-            "findings": f"Analysis incomplete: {str(e)}",
-            "5D": {},
-            "recommended_actions": [],
-            "confidence": {"level":"Error","reason": str(e)}
-        }
-
-# -------------------------
-# DOCX SAVE
-# -------------------------
-def save_docx(report_text, analyses):
-    os.makedirs("reports", exist_ok=True)
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"reports/ArmyIntel_5D_MDMP_Report_{timestamp}.docx"
-
-    doc = Document()
-
-    # Title Page
-    title = doc.add_heading("ARMYINTEL 5D–MDMP INTELLIGENCE REPORT", 0)
-    title.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-    doc.add_paragraph("Directorate of Military Intelligence (Training Use Only)", style='Intense Quote')
-    doc.add_paragraph(f"Report generated on: {datetime.now().strftime('%d %b %Y %H:%M:%S')}", style='Caption')
-    doc.add_paragraph("\n---\n", style='Normal')
-
-    # Individual Technique Analyses
-    for i, a in enumerate(analyses, 1):
-        doc.add_heading(f"{i}. {a.get('technique','Unknown')}", level=1)
-        doc.add_paragraph("Findings:", style='Heading 3')
-        doc.add_paragraph(a.get('findings',''), style='Normal')
-
-        doc.add_paragraph("5D Analysis:", style='Heading 3')
-        five_d = a.get('5D', {})
-        for d_key in ["Detect","Deter","Deny","Deliver","Destroy"]:
-            doc.add_paragraph(f"- {d_key}: {five_d.get(d_key,'')}", style='List Bullet')
-
-        doc.add_paragraph("Recommended Actions:", style='Heading 3')
-        if a.get('recommended_actions'):
-            for rec in a.get('recommended_actions', []):
-                doc.add_paragraph(f"- {rec}", style='List Bullet')
-        else:
-            doc.add_paragraph("- None provided / pending analysis", style='List Bullet')
-
-        confidence = a.get('confidence', {})
-        doc.add_paragraph(f"Confidence Level: {confidence.get('level','Unknown')}", style='Normal')
-        doc.add_paragraph(f"Reason: {confidence.get('reason','')}", style='Normal')
-        doc.add_paragraph("\n", style='Normal')
-
-    # MDMP Summary
-    doc.add_heading("MDMP Summary", level=1)
-    mdmp_summary = f"""
-MDMP Step Summary:
-1. Intelligence requirements derived from all {len(MILITARY_INTEL_TECHNIQUES)} disciplines.
-2. Threat courses of action evaluated using 5D framework.
-3. Red / Blue Team comparisons generated for decision support.
-4. Recommended defensive COAs prioritized for implementation.
-"""
-    doc.add_paragraph(mdmp_summary, style='Normal')
-
-    doc.save(filename)
-    return filename
-
-# -------------------------
-# FULL ANALYSIS ENGINE
-# -------------------------
-def run_full_analysis(problem_text):
-    analyses = []
-    with concurrent.futures.ThreadPoolExecutor(max_workers=7) as executor:
-        futures = {executor.submit(apply_5d_to_technique, tech, problem_text): tech for tech in MILITARY_INTEL_TECHNIQUES}
-        for future in concurrent.futures.as_completed(futures):
-            analyses.append(future.result())
-
-    fused = "\n\n".join([
-        f"{i+1}. {a.get('technique','Unknown')}\nFindings: {a.get('findings','')}"
-        for i, a in enumerate(analyses)
-    ])
-
-    mdmp = f"""
-MDMP Step Summary:
-1. Intelligence requirements synthesized across all {len(MILITARY_INTEL_TECHNIQUES)} methods.
-2. Defensive COAs refined via 5D reasoning.
-3. Enemy indicators and vulnerabilities mapped.
-4. Final recommendations structured for command review.
-"""
-    report_text = f"=== Combined Analysis ===\n{fused}\n\n=== MDMP Summary ===\n{mdmp}"
-    return report_text, analyses, fused, mdmp
-
-# -------------------------
-# GRADIO UI
-# -------------------------
-def analyze_problem(problem_text):
-    if not problem_text.strip():
-        problem_text = "Default scenario: enemy infiltration risk in forward area; develop full intelligence picture."
-    report_text, analyses, fused, mdmp = run_full_analysis(problem_text)
-    filename = save_docx(report_text, analyses)
-    return filename
-
-demo = gr.Interface(
-    fn=analyze_problem,
-    inputs=gr.Textbox(label="Enter Operational Problem or Training Scenario", lines=10),
-    outputs=gr.File(label="Download Intelligence Report"),
-    title="ARMYINTEL 5D–MDMP Intelligence Engine",
-    description="Enter any enemy or threat-related situation to generate a structured military intelligence report using 21 collection techniques and the 5D + MDMP frameworks."
-)
+        return f"⚠️ Error fetching response: {e}"
+
+# ------------------------------------------------------------
+# Knowledge Base – Editable Examples
+# ------------------------------------------------------------
+KNOWLEDGE_BASE = {
+    "Research Guidance": [
+        "How to select a PhD topic in Hindi literature.",
+        "Common pitfalls in literary analysis.",
+        "Structuring dissertation chapters.",
+        "Citation & referencing in Hindi research.",
+    ],
+    "Comparative Literature": [
+        "Hindi–Bengali modernist influences.",
+        "Urdu–Hindi poetic dialogue.",
+        "Translation as interpretation.",
+        "Cross-cultural motifs in modern 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 Function
+# ------------------------------------------------------------
+def mentorship_interface(domain, audience, query, language):
+    return generate_response(domain, query, audience, language)
+
+# ------------------------------------------------------------
+# Build Gradio App
+# ------------------------------------------------------------
+with gr.Blocks(title=APP_TITLE, theme=gr.themes.Soft()) as demo:
+    gr.Markdown(f"# 🌸 {APP_TITLE}")
+    gr.Markdown(APP_DESCRIPTION)
+
+    # ---------- A ----------
+    with gr.Tab("A · Study Modules & Guidance"):
+        domain = gr.Dropdown(DOMAINS, label="Select Domain")
+        audience = gr.Dropdown(AUDIENCES, label="Audience Type")
+        query = gr.TextArea(label="Enter your academic query", lines=4)
+        language = gr.Radio(["English", "Hindi"], label="Response Language", value="English")
+        output = gr.TextArea(label="Professor’s Response", lines=12)
+        gr.Button("Ask for Guidance").click(
+            mentorship_interface, [domain, audience, query, language], output
+        )
+
+    # ---------- B ----------
+    with gr.Tab("B · Research Mentorship"):
+        gr.Markdown("💡 Detailed mentoring on research design and methodology:")
+        for i in KNOWLEDGE_BASE["Research Guidance"]:
+            gr.Markdown(f"- {i}")
+
+    # ---------- C ----------
+    with gr.Tab("C · Comparative Literature Lab"):
+        gr.Markdown("🔶 Explore intersections of Hindi with other literatures:")
+        for i in KNOWLEDGE_BASE["Comparative Literature"]:
+            gr.Markdown(f"- {i}")
+
+    # ---------- D ----------
+    with gr.Tab("D · Annotated Texts & Commentaries"):
+        gr.Markdown("📘 Selected texts and professorial commentaries:")
+        for i in KNOWLEDGE_BASE["Commentaries"]:
+            gr.Markdown(f"- {i}")
+
+    # ---------- E ----------
+    with gr.Tab("E · Recorded Intellectual Presence"):
+        gr.Markdown("🎙️ Placeholder for audio/video lectures and reflections.")
+        gr.Markdown("_Coming soon — archival recordings of the Professor._")
+
+    # ---------- F ----------
+    with gr.Tab("F · Ask the Professor"):
+        gr.Markdown("✍️ Submit questions for asynchronous academic discussion:")
+        question = gr.TextArea(label="Your Question", lines=3)
+        response = gr.TextArea(label="Response Area", lines=8)
+        gr.Button("Submit Question").click(
+            mentorship_interface, [domain, audience, question, language], response
+        )
+
+    # ---------- G ----------
+    with gr.Tab("Audience & Contribution"):
+        gr.Markdown(
+            "👥 Former students and scholars may contribute annotated notes and comparative findings, "
+            "building the ongoing digital *parampara* of Hindi studies."
+        )
 
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+demo.launch(server_name="0.0.0.0", server_port=7860, share=True)