Update main.py

main.py

@@ -89,9 +89,9 @@ def decode_content(raw: str) -> str:
 # ── Request / Response Models ─────────────────────────────────────────────────
 class ProblemInput(BaseModel):
     content: str
-    intern_name: Optional[str] = "Intern"
-    intern_role: Optional[str] = "AI Developer Intern"
-    intern_goal: Optional[str] = ""
+    user_name: Optional[str] = "Anonymous"
+    user_role: Optional[str] = ""
+    user_goal: Optional[str] = ""
 
 
 class AgentOutput(BaseModel):
@@ -177,7 +177,7 @@ 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.
+You receive the full analysis. Your job: give the person 3 concrete next actions.
 
 Output format:
 ## Your Next Steps
@@ -200,11 +200,11 @@ Output format:
 **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."""
+Be specific enough that the person 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 are an encouraging but honest coach helping the person think more clearly.
 You receive the original problem input + full analysis.
 
 Output format:
@@ -221,10 +221,10 @@ Output format:
 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.
+The single most important mindset or approach shift for this person.
 
-### For Your Next Meeting
-3 specific things to do differently next time you face this type of problem.
+### For Next Time
+3 specific things to do differently next time this type of problem comes up.
 
 Keep it encouraging but honest. Max 250 words."""
 
@@ -248,9 +248,9 @@ 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'}
+Name: {name}
+Role: {role if role else "Not specified"}
+Goal: {goal if goal else "Not specified"}
 
 --- INPUT ---
 {content[:8000]}
@@ -281,7 +281,7 @@ Current Goal: {goal if goal else 'Not specified'}
     # Agent 4: Action Planner (receives everything so far)
     action_plan = run_agent(
         AGENT_ACTION_PLANNER,
-        f"""Intern Role: {role}\nIntern Goal: {goal}
+        f"""Role: {role}\nGoal: {goal}
 
 Problem Statement:\n{problem_statement}
 
@@ -294,7 +294,7 @@ Solutions:\n{solutions}""",
     # Agent 5: Thinking Coach (sees original input + full analysis)
     thinking_feedback = run_agent(
         AGENT_THINKING_COACH,
-        f"""Original Input from Intern:\n{content[:3000]}
+        f"""Original Input:\n{content[:3000]}
 
 Problem Analysis:\n{problem_statement}
 
@@ -457,9 +457,9 @@ def analyze(body: ProblemInput):
     try:
         result = run_pipeline(
             content=content,
-            name=body.intern_name or "Intern",
-            role=body.intern_role or "AI Developer Intern",
-            goal=body.intern_goal or "",
+            name=body.user_name or "Anonymous",
+            role=body.user_role or "",
+            goal=body.user_goal or "",
         )
         return result
     except anthropic.AuthenticationError:
@@ -490,9 +490,9 @@ def analyze_stream(body: ProblemInput):
 
         context = {
             "content": resolved_content[:8000],
-            "name": body.intern_name or "Intern",
-            "role": body.intern_role or "AI Developer Intern",
-            "goal": body.intern_goal or "",
+            "name": body.user_name or "Anonymous",
+            "role": body.user_role or "",
+            "goal": body.user_goal or "",
         }
         accumulated = {}
 
@@ -502,7 +502,7 @@ def analyze_stream(body: ProblemInput):
 
             # Build context-aware prompt for this agent
             if key == "analyst":
-                user_msg = f"Intern: {context['name']} | Role: {context['role']} | Goal: {context['goal']}\n\nAnalyze this content:\n{context['content']}"
+                user_msg = f"Name: {context['name']} | Role: {context['role']} | Goal: {context['goal']}\n\nAnalyze this content:\n{context['content']}"
             elif key == "root_cause":
                 user_msg = f"Problem:\n{accumulated.get('analyst','')}\n\nOriginal:\n{context['content'][:2000]}"
             elif key == "solutions":
@@ -556,8 +556,8 @@ def analyze_pdf(body: ProblemInput):
         )
         pdf_bytes = build_pdf(
             analysis,
-            name=body.intern_name or "Intern",
-            role=body.intern_role or "AI Developer Intern",
+            name=body.user_name or "Anonymous",
+            role=body.user_role or "",
         )
         filename = f"problem_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf"
         return StreamingResponse(