Update app.py

app.py

@@ -1,464 +1,603 @@
+"""
+Intern Problem-Solving API
+Multi-agent FastAPI backend for structured problem analysis and solution generation.
+Agents: Analyst → Root Cause → Solution Brainstorm → Action Planner → PDF Generator
+"""
+
+import io
+import json
 import os
-import gradio as gr
+import re
+from datetime import datetime
+from typing import Optional
+
 import anthropic
-from pypdf import PdfReader
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from reportlab.lib import colors
+from reportlab.lib.pagesizes import A4
+from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
+from reportlab.lib.units import mm
+from reportlab.platypus import (
+    HRFlowable,
+    Paragraph,
+    SimpleDocTemplate,
+    Spacer,
+    Table,
+    TableStyle,
+)
 
-# ── Anthropic client ──────────────────────────────────────────────────────────
-client = anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
+# ── App Setup ─────────────────────────────────────────────────────────────────
+app = FastAPI(
+    title="Intern Problem-Solving API",
+    description="Multi-agent system: Analyst → Root Cause → Solutions → Action Plan",
+    version="1.0.0",
+)
 
-SYSTEM_PROMPT = """You are an expert problem analyst and innovation coach helping interns understand real-world problems and brainstorm creative solutions.
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-When given a transcription or document, you will:
+client = anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY", ""))
 
-1. **PROBLEM UNDERSTANDING**
-   - Identify and clearly articulate the core problem being discussed
-   - Highlight key pain points, stakeholders, and context
-   - Summarize what is known vs unknown about the problem
+# ── Request / Response Models ─────────────────────────────────────────────────
+class ProblemInput(BaseModel):
+    content: str
+    intern_name: Optional[str] = "Intern"
+    intern_role: Optional[str] = "AI Developer Intern"
+    intern_goal: Optional[str] = ""
 
-2. **ROOT CAUSE ANALYSIS**
-   - Break down WHY this problem exists
-   - Identify underlying causes (technical, organizational, process-related)
 
-3. **BRAINSTORMING IDEAS**
-   - Generate 5-8 diverse, creative solution directions
-   - Include both quick wins and long-term strategic ideas
-   - Think across technology, process, and people dimensions
+class AgentOutput(BaseModel):
+    agent: str
+    output: str
 
-4. **NEXT STEPS FOR INTERNS**
-   - Suggest 3 concrete actions the intern can take immediately to explore this further
 
-Keep your tone encouraging, clear, and structured. Use headers and bullet points for readability."""
+class FullAnalysis(BaseModel):
+    problem_statement: str
+    root_causes: str
+    solutions: str
+    action_plan: str
+    thinking_feedback: str
 
 
-def extract_text_from_pdf(pdf_file) -> str:
-    reader = PdfReader(pdf_file)
-    pages_text = []
-    for page in reader.pages:
-        text = page.extract_text()
-        if text:
-            pages_text.append(text.strip())
-    return "\n\n".join(pages_text)
+# ── Agent Definitions ─────────────────────────────────────────────────────────
 
+AGENT_ANALYST = """You are the Problem Analyst Agent.
+Your ONLY job: read the input and produce a crisp, structured problem statement.
 
-def run_agent(text_input: str, pdf_file, progress=gr.Progress()):
-    content = ""
+Output format (use these exact headers):
+## Core Problem
+One clear sentence: who has what problem, in what context.
 
-    if pdf_file is not None:
-        progress(0.1, desc="Reading PDF...")
-        try:
-            content = extract_text_from_pdf(pdf_file)
-            if not content.strip():
-                yield "Could not extract text from this PDF. It may be scanned or image-based. Please paste the text manually."
-                return
-        except Exception as e:
-            yield f"Error reading PDF: {str(e)}"
-            return
+## Key Pain Points
+- Bullet each distinct pain point (max 5)
 
-    elif text_input and text_input.strip():
-        content = text_input.strip()
+## Stakeholders
+- Who is affected and how
 
-    else:
-        yield "Please paste a transcription or upload a PDF file to get started."
-        return
+## Known vs Unknown
+- Known: what facts are clear
+- Unknown: what gaps exist
 
-    if len(content) < 50:
-        yield "The input seems too short. Please provide more context for a meaningful analysis."
-        return
+Keep it factual. No solutions yet. Max 250 words."""
 
-    MAX_CHARS = 12000
-    if len(content) > MAX_CHARS:
-        content = content[:MAX_CHARS] + "\n\n[... content truncated for length ...]"
 
-    progress(0.3, desc="Analyzing problem...")
+AGENT_ROOT_CAUSE = """You are the Root Cause Analysis Agent.
+You receive a problem statement. Your job: find WHY it exists.
 
-    user_message = f"""Here is the transcription / document content to analyze:
+Output format:
+## Root Cause Analysis
 
----
-{content}
----
+### Immediate Cause
+What is the visible trigger of the problem?
 
-Please provide a full problem analysis and brainstorming session based on this content."""
+### Underlying Causes
+Break down causes across three lenses:
+- **Technical**: systems, tools, architecture issues
+- **Process**: workflow, communication, or procedural gaps
+- **People/Skills**: knowledge gaps, habits, capacity issues
 
-    output = ""
-    try:
-        with client.messages.stream(
-            model="claude-sonnet-4-6",
-            max_tokens=2048,
-            system=SYSTEM_PROMPT,
-            messages=[{"role": "user", "content": user_message}],
-        ) as stream:
-            progress(0.5, desc="Generating insights...")
-            for text_chunk in stream.text_stream:
-                output += text_chunk
-                yield output
+### The Real Root Cause
+One sentence: the deepest cause everything traces back to.
 
-    except anthropic.AuthenticationError:
-        yield "Invalid API key. Please set your ANTHROPIC_API_KEY in the Space secrets."
-    except anthropic.RateLimitError:
-        yield "Rate limit reached. Please wait a moment and try again."
-    except Exception as e:
-        yield f"Unexpected error: {str(e)}"
-
-
-# ── Custom CSS ────────────────────────────────────────────────────────────────
-custom_css = """
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&display=swap');
-
-* {
-    font-family: 'Inter', sans-serif !important;
-    box-sizing: border-box;
-}
-
-body, .gradio-container {
-    background-color: #ffffff !important;
-    color: #111827 !important;
-}
-
-.gradio-container {
-    max-width: 1280px !important;
-    margin: 0 auto !important;
-    padding: 0 2rem !important;
-}
-
-/* ── Header ── */
-#app-header {
-    padding: 2.75rem 0 2rem;
-    border-bottom: 1px solid #e5e7eb;
-    margin-bottom: 2rem;
-}
-
-#app-header h1 {
-    font-size: 1.5rem;
-    font-weight: 600;
-    color: #111827;
-    letter-spacing: -0.025em;
-    margin: 0 0 0.35rem 0;
-    line-height: 1.3;
-}
-
-#app-header p {
-    font-size: 0.875rem;
-    color: #6b7280;
-    margin: 0;
-    font-weight: 400;
-    line-height: 1.5;
-}
-
-#header-tag {
-    display: inline-block;
-    font-size: 0.7rem;
-    font-weight: 600;
-    letter-spacing: 0.1em;
-    text-transform: uppercase;
-    color: #2563eb;
-    background: #eff6ff;
-    border: 1px solid #bfdbfe;
-    border-radius: 4px;
-    padding: 0.2rem 0.6rem;
-    margin-bottom: 0.85rem;
-}
-
-/* ── Panels ── */
-.input-panel, .output-panel {
-    background: #ffffff !important;
-    border: 1px solid #e5e7eb !important;
-    border-radius: 10px !important;
-    padding: 1.5rem !important;
-}
-
-/* ── Section label ── */
-.section-label {
-    font-size: 0.7rem !important;
-    font-weight: 600 !important;
-    text-transform: uppercase !important;
-    letter-spacing: 0.1em !important;
-    color: #9ca3af !important;
-    margin: 0 0 1rem 0 !important;
-}
-
-/* ── Textarea ── */
-textarea {
-    background: #f9fafb !important;
-    border: 1px solid #e5e7eb !important;
-    border-radius: 8px !important;
-    color: #111827 !important;
-    font-size: 0.875rem !important;
-    line-height: 1.75 !important;
-    padding: 0.875rem 1rem !important;
-    resize: vertical !important;
-    transition: border-color 0.15s, box-shadow 0.15s !important;
-}
-
-textarea:focus {
-    border-color: #2563eb !important;
-    outline: none !important;
-    box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.08) !important;
-    background: #ffffff !important;
-}
-
-textarea::placeholder {
-    color: #d1d5db !important;
-}
-
-/* ── File upload ── */
-.gr-file {
-    background: #f9fafb !important;
-    border: 1px dashed #d1d5db !important;
-    border-radius: 8px !important;
-    color: #6b7280 !important;
-}
-
-/* ── Tabs ── */
-.tab-nav {
-    border-bottom: 1px solid #e5e7eb !important;
-    margin-bottom: 1rem !important;
-    background: transparent !important;
-}
-
-.tab-nav button {
-    font-size: 0.8rem !important;
-    font-weight: 500 !important;
-    color: #9ca3af !important;
-    border: none !important;
-    background: transparent !important;
-    padding: 0.5rem 1rem 0.6rem !important;
-    border-radius: 0 !important;
-    letter-spacing: 0.01em !important;
-    transition: color 0.15s !important;
-}
-
-.tab-nav button:hover {
-    color: #374151 !important;
-}
-
-.tab-nav button.selected {
-    color: #2563eb !important;
-    border-bottom: 2px solid #2563eb !important;
-    font-weight: 600 !important;
-}
-
-/* ── Run button ── */
-#run-btn {
-    background: #2563eb !important;
-    color: #ffffff !important;
-    border: none !important;
-    border-radius: 8px !important;
-    font-size: 0.875rem !important;
-    font-weight: 600 !important;
-    padding: 0.7rem 1.5rem !important;
-    letter-spacing: 0.01em !important;
-    transition: background 0.15s ease, box-shadow 0.15s ease !important;
-    margin-top: 0.875rem !important;
-    width: 100% !important;
-    cursor: pointer !important;
-}
-
-#run-btn:hover {
-    background: #1d4ed8 !important;
-    box-shadow: 0 2px 8px rgba(37, 99, 235, 0.25) !important;
-}
-
-/* ── Output area ── */
-#output-area {
-    background: #f9fafb !important;
-    border: 1px solid #e5e7eb !important;
-    border-radius: 8px !important;
-    padding: 1.25rem 1.5rem !important;
-    min-height: 420px !important;
-    font-size: 0.875rem !important;
-    line-height: 1.8 !important;
-    color: #374151 !important;
-}
-
-#output-area h1, #output-area h2, #output-area h3 {
-    color: #111827 !important;
-    font-weight: 600 !important;
-    letter-spacing: -0.015em !important;
-    margin-top: 1.5rem !important;
-    margin-bottom: 0.5rem !important;
-}
-
-#output-area h2 {
-    font-size: 1rem !important;
-    border-bottom: 1px solid #f3f4f6 !important;
-    padding-bottom: 0.4rem !important;
-}
-
-#output-area strong {
-    color: #111827 !important;
-    font-weight: 600 !important;
-}
-
-#output-area ul, #output-area ol {
-    padding-left: 1.25rem !important;
-    color: #4b5563 !important;
-    margin: 0.4rem 0 !important;
-}
-
-#output-area li {
-    margin-bottom: 0.25rem !important;
-}
-
-#output-area p {
-    color: #4b5563 !important;
-}
-
-#output-area em {
-    color: #9ca3af !important;
-}
-
-/* ── Accordion ── */
-.gr-accordion {
-    background: #f9fafb !important;
-    border: 1px solid #e5e7eb !important;
-    border-radius: 8px !important;
-    margin-top: 0.875rem !important;
-}
-
-.gr-accordion .label-wrap span {
-    font-size: 0.8rem !important;
-    color: #6b7280 !important;
-    font-weight: 500 !important;
-}
-
-/* ── Labels ── */
-label, .gr-label {
-    color: #6b7280 !important;
-    font-size: 0.8rem !important;
-    font-weight: 500 !important;
-}
-
-/* ── Examples ── */
-.gr-examples {
-    margin-top: 1rem !important;
-}
-
-.gr-examples table {
-    background: #ffffff !important;
-    border: 1px solid #e5e7eb !important;
-    border-radius: 8px !important;
-    overflow: hidden !important;
-}
-
-.gr-examples td {
-    color: #6b7280 !important;
-    font-size: 0.8rem !important;
-    padding: 0.6rem 1rem !important;
-    border-bottom: 1px solid #f3f4f6 !important;
-}
-
-.gr-examples tr:hover td {
-    background: #f9fafb !important;
-    cursor: pointer !important;
-}
-
-/* ── Divider ── */
-.divider {
-    border: none !important;
-    border-top: 1px solid #e5e7eb !important;
-    margin: 2rem 0 1.5rem !important;
-}
-
-/* ── Scrollbar ── */
-::-webkit-scrollbar { width: 5px; }
-::-webkit-scrollbar-track { background: #f9fafb; }
-::-webkit-scrollbar-thumb { background: #e5e7eb; border-radius: 4px; }
-::-webkit-scrollbar-thumb:hover { background: #d1d5db; }
+Be specific. Use "because" chains to trace causes deeper. Max 200 words."""
+
+
+AGENT_SOLUTIONS = """You are the Solution Brainstorm Agent.
+You receive a problem + root cause. Generate diverse, creative solutions.
+
+Output format:
+## Solution Ideas
+
+### Quick Wins (Do this week)
+1. **[Name]** — What it is + why it helps
+
+### Medium-Term Fixes (Do this month)
+2. **[Name]** — What it is + why it helps
+3. **[Name]** — What it is + why it helps
+
+### Strategic / Long-Term
+4. **[Name]** — What it is + why it helps
+5. **[Name]** — What it is + why it helps
+
+### Unconventional / Creative
+6. **[Name]** — Think outside the box
+7. **[Name]** — Wildcard idea
+
+For each idea: name it, describe it in 1-2 sentences, state the trade-off.
+Think across: AI tools, process redesign, automation, collaboration, education.
+Max 300 words."""
+
+
+AGENT_ACTION_PLANNER = """You are the Action Planner Agent.
+You receive the full analysis. Your job: give the intern 3 concrete next actions.
+
+Output format:
+## Your Next Steps
+
+### Action 1: [Do This Today]
+**What exactly**: One sentence instruction
+**How**: Step-by-step (3-4 steps max)
+**Success looks like**: How you'll know it worked
+**Time needed**: X hours
+
+### Action 2: [Do This Week]
+**What exactly**: One sentence instruction
+**How**: Step-by-step (3-4 steps max)
+**Success looks like**: How you'll know it worked
+**Time needed**: X hours
+
+### Action 3: [Do This Month]
+**What exactly**: One sentence instruction
+**How**: Step-by-step (3-4 steps max)
+**Success looks like**: How you'll know it worked
+**Time needed**: X hours
+
+Be specific enough that the intern can start immediately. No vague advice."""
+
+
+AGENT_THINKING_COACH = """You are the Thinking Coach Agent.
+You are an encouraging but honest coach helping an intern grow.
+You receive the original problem input + full analysis.
+
+Output format:
+## Thinking Feedback
+
+### What You Got Right
+- Specific things in how the problem was framed that show good thinking
+
+### Blind Spots to Watch
+- Where the framing was shallow or missing something important
+- Specific examples only — no generic observations
+
+### Are You Thinking Like a Problem Solver or Task Executor?
+One honest assessment with evidence from their input.
+
+### One Big Shift
+The single most important mindset or approach shift that will help this intern most.
+
+### For Your Next Meeting
+3 specific things to do differently next time you face this type of problem.
+
+Keep it encouraging but honest. Max 250 words."""
+
+
+# ── Core Agent Runner ─────────────────────────────────────────────────────────
+
+def run_agent(system_prompt: str, user_content: str, max_tokens: int = 800) -> str:
+    """Run a single agent and return its text output."""
+    response = client.messages.create(
+        model="claude-sonnet-4-6",
+        max_tokens=max_tokens,
+        system=system_prompt,
+        messages=[{"role": "user", "content": user_content}],
+    )
+    return response.content[0].text
+
+
+# ── Pipeline ──────────────────────────────────────────────────────────────────
+
+def run_pipeline(content: str, name: str, role: str, goal: str) -> FullAnalysis:
+    """Run all 5 agents in sequence, passing outputs forward."""
+
+    context_header = f"""
+Intern Name: {name}
+Intern Role: {role}
+Current Goal: {goal if goal else 'Not specified'}
+
+--- INPUT ---
+{content[:8000]}
+--- END INPUT ---
 """
 
-# ── Gradio UI ─────────────────────────────────────────────────────────────────
-with gr.Blocks(
-    title="Problem Brainstormer",
-    css=custom_css,
-) as demo:
-
-    gr.HTML("""
-        <div id="app-header">
-            <div id="header-tag">Intern Tool</div>
-            <h1>Problem Brainstormer</h1>
-            <p>Paste a meeting transcript or upload a document to get structured problem analysis and solution ideas.</p>
-        </div>
-    """)
-
-    with gr.Row(equal_height=False):
-        with gr.Column(scale=1, elem_classes=["input-panel"]):
-            gr.HTML('<p class="section-label">Input</p>')
-
-            with gr.Tabs():
-                with gr.Tab("Paste Text"):
-                    text_input = gr.Textbox(
-                        label="",
-                        placeholder="Paste your meeting notes, transcript, or problem description here...",
-                        lines=16,
-                        max_lines=32,
-                    )
-
-                with gr.Tab("Upload PDF"):
-                    pdf_input = gr.File(
-                        label="",
-                        file_types=[".pdf"],
-                        file_count="single",
-                    )
-                    gr.HTML('<p style="font-size:0.75rem;color:#9ca3af;margin-top:0.5rem">Text-based PDFs only. Scanned documents are not supported.</p>')
-
-            run_btn = gr.Button(
-                "Analyze and Brainstorm",
-                variant="primary",
-                elem_id="run-btn",
-            )
+    # Agent 1: Analyst
+    problem_statement = run_agent(
+        AGENT_ANALYST,
+        f"Analyze this problem:\n{context_header}",
+        max_tokens=600,
+    )
+
+    # Agent 2: Root Cause (receives problem statement)
+    root_causes = run_agent(
+        AGENT_ROOT_CAUSE,
+        f"Problem Statement:\n{problem_statement}\n\nOriginal input context:\n{content[:3000]}",
+        max_tokens=500,
+    )
 
-            with gr.Accordion("How to use", open=False):
-                gr.Markdown("""
-**Paste Text** — Copy any transcript, meeting notes, or problem statement into the text field.
+    # Agent 3: Solutions (receives problem + root causes)
+    solutions = run_agent(
+        AGENT_SOLUTIONS,
+        f"Problem Statement:\n{problem_statement}\n\nRoot Causes:\n{root_causes}",
+        max_tokens=700,
+    )
 
-**Upload PDF** — Upload a text-based PDF such as a spec, report, or research document.
+    # Agent 4: Action Planner (receives everything so far)
+    action_plan = run_agent(
+        AGENT_ACTION_PLANNER,
+        f"""Intern Role: {role}\nIntern Goal: {goal}
 
-**Click Analyze** to receive:
-- A clear articulation of the core problem
-- Root cause breakdown
-- 5–8 creative solution directions
-- Concrete next steps you can take as an intern
-                """)
+Problem Statement:\n{problem_statement}
 
-        with gr.Column(scale=1, elem_classes=["output-panel"]):
-            gr.HTML('<p class="section-label">Analysis</p>')
-            output = gr.Markdown(
-                value="*Your analysis will appear here once you submit.*",
-                elem_id="output-area",
-            )
+Root Causes:\n{root_causes}
 
-    gr.HTML('<hr class="divider">')
-    gr.HTML('<p class="section-label">Example Inputs</p>')
-
-    gr.Examples(
-        examples=[
-            [
-                "In today's standup, the team discussed that customers are complaining that the onboarding flow takes too long. New users drop off after the third step in the sign-up process. We don't have clear data on exactly which step causes the most drop-off. The product team thinks it might be the email verification step but engineering says it could be the profile setup form. We have about 2 weeks before the next release and the CEO wants this fixed before the investor demo.",
-                None,
-            ],
-            [
-                "Meeting transcript: So the main issue we're facing is that our data pipeline fails every Friday evening when batch jobs run. The on-call engineer has to manually restart it each time. It's been happening for three months. We've tried looking at the logs but they're not detailed enough. The infrastructure team is overwhelmed and can't prioritize it. Meanwhile the analytics team can't get their Monday morning reports on time which is affecting business decisions.",
-                None,
-            ],
-        ],
-        inputs=[text_input, pdf_input],
-        label="",
+Solutions:\n{solutions}""",
+        max_tokens=700,
+    )
+
+    # Agent 5: Thinking Coach (sees original input + full analysis)
+    thinking_feedback = run_agent(
+        AGENT_THINKING_COACH,
+        f"""Original Input from Intern:\n{content[:3000]}
+
+Problem Analysis:\n{problem_statement}
+
+Root Causes:\n{root_causes}""",
+        max_tokens=600,
     )
 
-    run_btn.click(
-        fn=run_agent,
-        inputs=[text_input, pdf_input],
-        outputs=output,
-        show_progress="full",
+    return FullAnalysis(
+        problem_statement=problem_statement,
+        root_causes=root_causes,
+        solutions=solutions,
+        action_plan=action_plan,
+        thinking_feedback=thinking_feedback,
     )
 
-    text_input.submit(
-        fn=run_agent,
-        inputs=[text_input, pdf_input],
-        outputs=output,
-        show_progress="full",
+
+# ── PDF Generator ─────────────────────────────────────────────────────────────
+
+def strip_markdown(text: str) -> str:
+    """Remove markdown bold/italic markers for plain PDF text."""
+    text = re.sub(r"\*\*(.*?)\*\*", r"\1", text)
+    text = re.sub(r"\*(.*?)\*", r"\1", text)
+    return text
+
+
+def build_pdf(analysis: FullAnalysis, name: str, role: str) -> bytes:
+    """Generate a professional PDF report from the analysis."""
+    buf = io.BytesIO()
+
+    # ── Page setup
+    doc = SimpleDocTemplate(
+        buf,
+        pagesize=A4,
+        rightMargin=20 * mm,
+        leftMargin=20 * mm,
+        topMargin=22 * mm,
+        bottomMargin=22 * mm,
+    )
+
+    styles = getSampleStyleSheet()
+    W = A4[0] - 40 * mm  # usable width
+
+    # ── Custom styles
+    s_title = ParagraphStyle(
+        "title",
+        parent=styles["Normal"],
+        fontSize=22,
+        fontName="Helvetica-Bold",
+        textColor=colors.HexColor("#1a1a2e"),
+        spaceAfter=4,
+    )
+    s_sub = ParagraphStyle(
+        "sub",
+        parent=styles["Normal"],
+        fontSize=11,
+        fontName="Helvetica",
+        textColor=colors.HexColor("#6b7280"),
+        spaceAfter=12,
+    )
+    s_section = ParagraphStyle(
+        "section",
+        parent=styles["Normal"],
+        fontSize=13,
+        fontName="Helvetica-Bold",
+        textColor=colors.HexColor("#2563eb"),
+        spaceBefore=16,
+        spaceAfter=6,
+    )
+    s_h3 = ParagraphStyle(
+        "h3",
+        parent=styles["Normal"],
+        fontSize=11,
+        fontName="Helvetica-Bold",
+        textColor=colors.HexColor("#374151"),
+        spaceBefore=8,
+        spaceAfter=3,
+    )
+    s_body = ParagraphStyle(
+        "body",
+        parent=styles["Normal"],
+        fontSize=10,
+        fontName="Helvetica",
+        textColor=colors.HexColor("#374151"),
+        leading=15,
+        spaceAfter=4,
+    )
+    s_label = ParagraphStyle(
+        "label",
+        parent=styles["Normal"],
+        fontSize=8,
+        fontName="Helvetica-Bold",
+        textColor=colors.white,
+    )
+
+    def agent_badge(label: str, color: str) -> Table:
+        """Small colored badge showing which agent produced this section."""
+        data = [[Paragraph(f"◉  {label}", s_label)]]
+        t = Table(data, colWidths=[W])
+        t.setStyle(
+            TableStyle(
+                [
+                    ("BACKGROUND", (0, 0), (-1, -1), colors.HexColor(color)),
+                    ("TOPPADDING", (0, 0), (-1, -1), 5),
+                    ("BOTTOMPADDING", (0, 0), (-1, -1), 5),
+                    ("LEFTPADDING", (0, 0), (-1, -1), 10),
+                    ("ROUNDEDCORNERS", [4, 4, 4, 4]),
+                ]
+            )
+        )
+        return t
+
+    def render_markdown_block(md_text: str) -> list:
+        """Convert basic markdown to ReportLab flowables."""
+        flowables = []
+        for line in md_text.splitlines():
+            line = line.strip()
+            if not line:
+                flowables.append(Spacer(1, 4))
+                continue
+            if line.startswith("## "):
+                flowables.append(Paragraph(line[3:], s_section))
+            elif line.startswith("### "):
+                flowables.append(Paragraph(line[4:], s_h3))
+            elif line.startswith("- ") or line.startswith("* "):
+                clean = strip_markdown(line[2:])
+                flowables.append(Paragraph(f"• {clean}", s_body))
+            elif re.match(r"^\d+\.", line):
+                clean = strip_markdown(line)
+                flowables.append(Paragraph(clean, s_body))
+            elif line.startswith("**") and line.endswith("**"):
+                flowables.append(Paragraph(line[2:-2], s_h3))
+            else:
+                clean = strip_markdown(line)
+                flowables.append(Paragraph(clean, s_body))
+        return flowables
+
+    story = []
+
+    # ── Header
+    story.append(Paragraph("Problem Analysis Report", s_title))
+    story.append(
+        Paragraph(
+            f"{name}  ·  {role}  ·  {datetime.now().strftime('%d %B %Y')}",
+            s_sub,
+        )
+    )
+    story.append(HRFlowable(width=W, thickness=1.5, color=colors.HexColor("#2563eb"), spaceAfter=10))
+
+    # ── Section 1: Problem Statement
+    story.append(agent_badge("AGENT 1  —  Problem Analyst", "#1e3a5f"))
+    story.append(Spacer(1, 6))
+    story += render_markdown_block(analysis.problem_statement)
+
+    # ── Section 2: Root Cause
+    story.append(Spacer(1, 8))
+    story.append(agent_badge("AGENT 2  —  Root Cause Analyst", "#1a4731"))
+    story.append(Spacer(1, 6))
+    story += render_markdown_block(analysis.root_causes)
+
+    # ── Section 3: Solutions
+    story.append(Spacer(1, 8))
+    story.append(agent_badge("AGENT 3  —  Solution Brainstorm", "#4a1942"))
+    story.append(Spacer(1, 6))
+    story += render_markdown_block(analysis.solutions)
+
+    # ── Section 4: Action Plan
+    story.append(Spacer(1, 8))
+    story.append(agent_badge("AGENT 4  —  Action Planner", "#7c2d12"))
+    story.append(Spacer(1, 6))
+    story += render_markdown_block(analysis.action_plan)
+
+    # ── Section 5: Thinking Coach
+    story.append(Spacer(1, 8))
+    story.append(agent_badge("AGENT 5  —  Thinking Coach", "#312e81"))
+    story.append(Spacer(1, 6))
+    story += render_markdown_block(analysis.thinking_feedback)
+
+    # ── Footer note
+    story.append(Spacer(1, 16))
+    story.append(HRFlowable(width=W, thickness=0.5, color=colors.HexColor("#e5e7eb"), spaceAfter=6))
+    story.append(
+        Paragraph(
+            "Generated by the AI Intern Problem-Solving System  ·  Confidential",
+            ParagraphStyle("footer", parent=styles["Normal"], fontSize=8, textColor=colors.HexColor("#9ca3af"), alignment=1),
+        )
     )
 
+    doc.build(story)
+    buf.seek(0)
+    return buf.read()
+
+
+# ── API Endpoints ─────────────────────────────────────────────────────────────
+
+@app.get("/")
+def root():
+    return {
+        "service": "Intern Problem-Solving API",
+        "version": "1.0.0",
+        "agents": [
+            "1. Problem Analyst",
+            "2. Root Cause Analyst",
+            "3. Solution Brainstorm",
+            "4. Action Planner",
+            "5. Thinking Coach",
+        ],
+        "endpoints": {
+            "POST /analyze": "Run full 5-agent pipeline, returns JSON",
+            "POST /analyze/stream": "Stream analysis as server-sent events",
+            "POST /analyze/pdf": "Run pipeline + return downloadable PDF",
+            "GET /health": "Health check",
+        },
+    }
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "timestamp": datetime.utcnow().isoformat()}
+
+
+@app.post("/analyze", response_model=FullAnalysis)
+def analyze(body: ProblemInput):
+    """Run full 5-agent pipeline. Returns structured JSON."""
+    if not body.content.strip():
+        raise HTTPException(status_code=400, detail="Content cannot be empty.")
+    if len(body.content) < 30:
+        raise HTTPException(status_code=400, detail="Content too short for meaningful analysis.")
+
+    try:
+        result = run_pipeline(
+            content=body.content,
+            name=body.intern_name or "Intern",
+            role=body.intern_role or "AI Developer Intern",
+            goal=body.intern_goal or "",
+        )
+        return result
+    except anthropic.AuthenticationError:
+        raise HTTPException(status_code=401, detail="Invalid Anthropic API key.")
+    except anthropic.RateLimitError:
+        raise HTTPException(status_code=429, detail="Rate limit reached. Please wait and retry.")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/analyze/stream")
+def analyze_stream(body: ProblemInput):
+    """Stream each agent's output as server-sent events (SSE)."""
+    if not body.content.strip():
+        raise HTTPException(status_code=400, detail="Content cannot be empty.")
+
+    def event_stream():
+        agents = [
+            ("analyst", AGENT_ANALYST, "Problem Analyst"),
+            ("root_cause", AGENT_ROOT_CAUSE, "Root Cause Analyst"),
+            ("solutions", AGENT_SOLUTIONS, "Solution Brainstorm"),
+            ("action_plan", AGENT_ACTION_PLANNER, "Action Planner"),
+            ("thinking", AGENT_THINKING_COACH, "Thinking Coach"),
+        ]
+
+        context = {
+            "content": body.content[:8000],
+            "name": body.intern_name or "Intern",
+            "role": body.intern_role or "AI Developer Intern",
+            "goal": body.intern_goal or "",
+        }
+        accumulated = {}
+
+        for key, system_prompt, label in agents:
+            # Send agent start event
+            yield f"data: {json.dumps({'event': 'agent_start', 'agent': key, 'label': label})}\n\n"
+
+            # Build context-aware prompt for this agent
+            if key == "analyst":
+                user_msg = f"Intern: {context['name']} | Role: {context['role']} | Goal: {context['goal']}\n\nAnalyze:\n{context['content']}"
+            elif key == "root_cause":
+                user_msg = f"Problem:\n{accumulated.get('analyst','')}\n\nOriginal:\n{context['content'][:2000]}"
+            elif key == "solutions":
+                user_msg = f"Problem:\n{accumulated.get('analyst','')}\n\nRoot Causes:\n{accumulated.get('root_cause','')}"
+            elif key == "action_plan":
+                user_msg = f"Role: {context['role']}\n\nProblem:\n{accumulated.get('analyst','')}\n\nCauses:\n{accumulated.get('root_cause','')}\n\nSolutions:\n{accumulated.get('solutions','')}"
+            else:
+                user_msg = f"Original Input:\n{context['content'][:2500]}\n\nProblem:\n{accumulated.get('analyst','')}\n\nCauses:\n{accumulated.get('root_cause','')}"
+
+            # Stream this agent's output
+            agent_text = ""
+            with client.messages.stream(
+                model="claude-sonnet-4-6",
+                max_tokens=800,
+                system=system_prompt,
+                messages=[{"role": "user", "content": user_msg}],
+            ) as stream:
+                for chunk in stream.text_stream:
+                    agent_text += chunk
+                    yield f"data: {json.dumps({'event': 'token', 'agent': key, 'text': chunk})}\n\n"
+
+            accumulated[key] = agent_text
+            yield f"data: {json.dumps({'event': 'agent_done', 'agent': key})}\n\n"
+
+        yield f"data: {json.dumps({'event': 'done'})}\n\n"
+
+    return StreamingResponse(
+        event_stream(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "X-Accel-Buffering": "no",
+        },
+    )
+
+
+@app.post("/analyze/pdf")
+def analyze_pdf(body: ProblemInput):
+    """Run full pipeline and return a downloadable PDF report."""
+    if not body.content.strip():
+        raise HTTPException(status_code=400, detail="Content cannot be empty.")
+
+    try:
+        analysis = run_pipeline(
+            content=body.content,
+            name=body.intern_name or "Intern",
+            role=body.intern_role or "AI Developer Intern",
+            goal=body.intern_goal or "",
+        )
+        pdf_bytes = build_pdf(
+            analysis,
+            name=body.intern_name or "Intern",
+            role=body.intern_role or "AI Developer Intern",
+        )
+        filename = f"problem_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf"
+        return StreamingResponse(
+            io.BytesIO(pdf_bytes),
+            media_type="application/pdf",
+            headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+        )
+    except anthropic.AuthenticationError:
+        raise HTTPException(status_code=401, detail="Invalid Anthropic API key.")
+    except anthropic.RateLimitError:
+        raise HTTPException(status_code=429, detail="Rate limit. Please wait and retry.")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
 
+# ── Dev Runner ────────────────────────────────────────────────────────────────
 if __name__ == "__main__":
-    demo.launch()
+    import uvicorn
+    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True)