Upload app (46).py

app (46).py

@@ -0,0 +1,212 @@
+# app.py — Kashmir AO Action Plan for Inf (Full Version)
+import os
+import gradio as gr
+from openai import OpenAI
+
+# ---------------------------
+# Setup OpenAI
+# ---------------------------
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# ---------------------------
+# Knowledge Base placeholder
+# ---------------------------
+KNOWLEDGE_BASE = """
+Embedded doctrinal and operational notes for Kashmir AO.
+"""
+
+# ---------------------------
+# Helper functions
+# ---------------------------
+def generate_standard_report(answers):
+    """Generate standard analyst report from answers + KB context."""
+    text_blob = "\n".join(f"{q}: {a}" for q, a in answers.items())
+    prompt = f"""You are a military analyst. Based on the following answers and knowledge base, write a structured, advisory style report for the commander.
+Knowledge Base:
+{KNOWLEDGE_BASE}
+
+Answers:
+{text_blob}
+
+Report:"""
+    try:
+        resp = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=500
+        )
+        return resp.choices[0].message.content.strip()
+    except Exception as e:
+        return f"[Error generating report: {e}]"
+
+def generate_enhanced_report(answers_a, answers_b, answers_c, gate):
+    """Generate enhanced analyst report (sectioned)."""
+    text_blob = "\n".join(
+        f"Section A: {answers_a}\nSection B: {answers_b}\nSection C: {answers_c}\nGate: {gate}"
+    )
+    prompt = f"""You are a senior staff officer. Based on the following sectioned answers and doctrinal context, prepare an advisory intelligence report for the commander.
+
+Knowledge Base:
+{KNOWLEDGE_BASE}
+
+Answers:
+{text_blob}
+
+Report:"""
+    try:
+        resp = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=600
+        )
+        return resp.choices[0].message.content.strip()
+    except Exception as e:
+        return f"[Error generating enhanced report: {e}]"
+
+# ---------------------------
+# Threat Status Evaluator
+# ---------------------------
+def derive_threat_status(answers):
+    """Simple heuristic threat evaluation."""
+    status = "Green"
+    desc = "Prepared for peacetime security; no immediate or future attack indicators."
+    text_blob = " ".join(str(v).lower() for v in answers.values())
+    if any(word in text_blob for word in ["engaged", "shooting", "contact", "attack now", "ambush"]):
+        status = "Red"
+        desc = "Ready for ongoing attack — hostile activity confirmed."
+    elif any(word in text_blob for word in ["imminent", "soon", "moving towards", "likely attack", "advance"]):
+        status = "Orange"
+        desc = "Prepared for imminent attack — strong indicators from inputs."
+    elif any(word in text_blob for word in ["possible", "expected", "threat", "risk", "movement detected"]):
+        status = "Blue"
+        desc = "Ready for anticipated short-term attack probability."
+    return status, desc
+
+# ---------------------------
+# Doctrinal summary constant
+# ---------------------------
+DOCTRINAL_SUMMARY_DETAILED = """
+# 📘 Doctrinal Summary (Sanitized, Detailed)
+
+Purpose and Approach
+- Intelligence is treated as a combat function, not just support. Adversaries (cartels, terrorists, insurgents) have quasi-military structures that must be mapped and targeted using intelligence architectures.
+- Objective: Give the commander a persistent, adaptive situational picture by embedding Command, Control, Communications, Intelligence, Surveillance, Reconnaissance (C3ISR) across the AO.
+- Scope: Covers architecture, zoning, collection posture, force protection, staff planning, and doctrinal lenses. No tactical timings or unit counts are provided here.
+
+Doctrinal Lenses
+- Threat vs. Situational Awareness: Distinguish enemy threat indicators from the commander’s broader situational awareness.
+- The 5Ds (Detect, Deter, Deny, Deliver, Destroy): Each lens shifts how intelligence and reconnaissance assets should be tasked.
+- Red Teaming & Vulnerability Analysis: Commanders must ask not only what is seen but why something was *not* seen.
+
+Command & Control Embedding
+- Rear Area: Secure HQ and sustainment.
+- Forward Areas: Distributed tactical nodes with reconnaissance, HUMINT, and CI integration.
+- Deep Forward: Covert placements close to adversary leadership or logistics.
+- Tactical Ops Centers (TOCs): Covert urban/rural apartments acting as intelligence fusion and relay nodes.
+
+Force Protection
+- Randomize movement, guard routines, and convoy timings.
+- Use counterintelligence not only to detect spies but also to map and attack enemy intent and surveillance.
+
+Collection Integration
+- HUMINT, TACHUMINT, Reconnaissance, Surveillance, and CI nodes all feed Tactical Ops Centers.
+- SIIE (Structured Integrated Intelligence Environment) ensures commanders see a fused picture, not fragments.
+
+Staff Guidance
+- Align staff planning with commander’s intent *and* with adversary threat reality.
+- Avoid overreliance on external MI units; embed unit-level intelligence.
+- Differentiate between Advance Warning, Surprise, and Situational Awareness — they are not the same.
+
+Outcome
+- Commanders who apply these doctrines convert intelligence from passive reporting into an active weapon system.
+- Security forces must think of intelligence as combat, structuring AO into zones, embedding covert/ overt nodes, and preempting threats before they manifest.
+"""
+
+# ---------------------------
+# Build Gradio UI
+# ---------------------------
+with gr.Blocks() as demo:
+    gr.HTML('<img src="https://huggingface.co/spaces/Militaryint/ops/resolve/main/banner.png" width="100%">')
+    gr.Markdown("# Kashmir AO Action Plan for Inf — ARMY Analyst Tool & Battle Planner")
+
+    # ----------- Standard Analyst Tab -----------
+    with gr.Tab("Standard Analyst"):
+        std_questions = [
+            "When/where was the enemy sighted?",
+            "Direction of movement?",
+            "Enemy size and strength?",
+            "Weapons and equipment observed?",
+            "Vehicles or on foot?",
+            "Proximity to roads?",
+            "Proximity to camps?",
+            "Support of locals?",
+            "Enemy formation/disposition?",
+            "Terrain type?"
+        ]
+        std_inputs = [gr.Textbox(label=q) for q in std_questions]
+        std_btn = gr.Button("Generate Standard Report")
+        std_output = gr.Markdown()
+        def on_std_click(*answers):
+            return generate_standard_report(dict(zip(std_questions, answers)))
+        std_btn.click(on_std_click, inputs=std_inputs, outputs=std_output)
+
+    # ----------- Enhanced Analyst Tab -----------
+    with gr.Tab("Enhanced Analyst (Sectioned)"):
+        ENH_SECTION_A = [
+            "Does the unit have Ops + Int integration?",
+            "Does the unit have intelligence SOPs?",
+            "Reconnaissance & Surveillance plan integrated?",
+            "Force Protection SOP & threat conditions?",
+            "Forward intelligence projection capability?"
+        ]
+        ENH_SECTION_B = [
+            "Vulnerability analysis tools in place?",
+            "Randomness in troop/officer movement?",
+            "Source vetting & counterintelligence registry?",
+            "Intelligence treated as doctrine repository?",
+            "CI used in base/ops vulnerability analysis?"
+        ]
+        ENH_SECTION_C = [
+            "Embedded intelligence in routine ops?",
+            "Advance warning system present?",
+            "Capability for deliberate vs quick ops?",
+            "Projected command/control nodes?",
+            "Distinction between SA, Advance Warn, Surprise?"
+        ]
+        a_inputs = [gr.Textbox(label=q) for q in ENH_SECTION_A]
+        b_inputs = [gr.Textbox(label=q) for q in ENH_SECTION_B]
+        c_inputs = [gr.Textbox(label=q) for q in ENH_SECTION_C]
+        gate_q = gr.Textbox(label="Does the commander want AI to supply the report?")
+        enh_btn = gr.Button("Generate Enhanced Report")
+        enh_output = gr.Markdown()
+        def on_enh_click(*args):
+            answers_a = dict(zip(ENH_SECTION_A, args[:len(ENH_SECTION_A)]))
+            answers_b = dict(zip(ENH_SECTION_B, args[len(ENH_SECTION_A):len(ENH_SECTION_A)+len(ENH_SECTION_B)]))
+            answers_c = dict(zip(ENH_SECTION_C, args[len(ENH_SECTION_A)+len(ENH_SECTION_B):-1]))
+            gate = args[-1]
+            return generate_enhanced_report(answers_a, answers_b, answers_c, gate)
+        enh_btn.click(on_enh_click, inputs=a_inputs+b_inputs+c_inputs+[gate_q], outputs=enh_output)
+
+    # ----------- Doctrinal Summary Tab -----------
+    with gr.Tab("Doctrinal Summary (Sanitized, Detailed)"):
+        gr.Markdown(DOCTRINAL_SUMMARY_DETAILED)
+
+    # ----------- Threat Status Tab (NEW) -----------
+    with gr.Tab("Threat Status"):
+        gr.Markdown("### 🛡️ Threat Status Indicator\nColors indicate readiness levels derived from your inputs in Standard/Enhanced tabs.")
+        status_out = gr.HTML("<div style='font-size:20px;'>Awaiting evaluation...</div>")
+        eval_btn = gr.Button("Evaluate Threat Status")
+        def on_eval():
+            # Placeholder, since live state wiring not connected
+            dummy_answers = {"sample": "no threat"}
+            status, desc = derive_threat_status(dummy_answers)
+            color_map = {"Red":"#FF4C4C","Orange":"#FFA500","Blue":"#1E90FF","Green":"#32CD32"}
+            html = f"<div style='padding:1em;border-radius:8px;background:{color_map[status]};color:white;font-size:20px;'><b>{status}</b>: {desc}</div>"
+            return html
+        eval_btn.click(on_eval, inputs=None, outputs=status_out)
+
+# ---------------------------
+# Launch app
+# ---------------------------
+if __name__ == "__main__":
+    demo.launch()