pyr0ball
9702646738
Pages were hardcoding DEFAULT_DB at import time, meaning cloud-mode
per-user DB routing was silently ignored. Pages affected:
1_Job_Review, 5_Interviews, 6_Interview_Prep, 7_Survey.
Adds resolve_session("peregrine") + get_db_path() pattern to each,
matching the pattern already used in 4_Apply.py.
Fixes #24.
277 lines
11 KiB
Python
277 lines
11 KiB
Python
# app/pages/7_Survey.py
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"""
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Survey Assistant — real-time help with culture-fit surveys.
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Supports text paste and screenshot (via clipboard or file upload).
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Quick mode: "pick B" + one-liner. Detailed mode: option-by-option breakdown.
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"""
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import base64
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import io
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import sys
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from datetime import datetime
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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import requests
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import streamlit as st
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from scripts.db import (
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DEFAULT_DB, init_db,
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get_interview_jobs, get_job_by_id,
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insert_survey_response, get_survey_responses,
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)
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from scripts.llm_router import LLMRouter
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from app.cloud_session import resolve_session, get_db_path
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resolve_session("peregrine")
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st.title("📋 Survey Assistant")
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init_db(get_db_path())
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# ── Vision service health check ────────────────────────────────────────────────
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def _vision_available() -> bool:
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try:
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r = requests.get("http://localhost:8002/health", timeout=2)
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return r.status_code == 200
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except Exception:
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return False
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vision_up = _vision_available()
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# ── Job selector ───────────────────────────────────────────────────────────────
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jobs_by_stage = get_interview_jobs(get_db_path())
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survey_jobs = jobs_by_stage.get("survey", [])
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other_jobs = (
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jobs_by_stage.get("applied", []) +
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jobs_by_stage.get("phone_screen", []) +
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jobs_by_stage.get("interviewing", []) +
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jobs_by_stage.get("offer", [])
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)
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all_jobs = survey_jobs + other_jobs
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if not all_jobs:
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st.info("No active jobs found. Add jobs in Job Review first.")
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st.stop()
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job_labels = {j["id"]: f"{j.get('company', '?')} — {j.get('title', '')}" for j in all_jobs}
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selected_job_id = st.selectbox(
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"Job",
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options=[j["id"] for j in all_jobs],
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format_func=lambda jid: job_labels[jid],
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index=0,
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)
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selected_job = get_job_by_id(get_db_path(), selected_job_id)
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# ── LLM prompt builders ────────────────────────────────────────────────────────
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_SURVEY_SYSTEM = (
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"You are a job application advisor helping a candidate answer a culture-fit survey. "
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"The candidate values collaborative teamwork, clear communication, growth, and impact. "
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"Choose answers that present them in the best professional light."
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)
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def _build_text_prompt(text: str, mode: str) -> str:
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if mode == "Quick":
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return (
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"Answer each survey question below. For each, give ONLY the letter of the best "
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"option and a single-sentence reason. Format exactly as:\n"
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"1. B — reason here\n2. A — reason here\n\n"
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f"Survey:\n{text}"
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)
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return (
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"Analyze each survey question below. For each question:\n"
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"- Briefly evaluate each option (1 sentence each)\n"
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"- State your recommendation with reasoning\n\n"
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f"Survey:\n{text}"
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)
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def _build_image_prompt(mode: str) -> str:
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if mode == "Quick":
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return (
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"This is a screenshot of a culture-fit survey. Read all questions and answer each "
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"with the letter of the best option for a collaborative, growth-oriented candidate. "
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"Format: '1. B — brief reason' on separate lines."
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)
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return (
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"This is a screenshot of a culture-fit survey. For each question, evaluate each option "
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"and recommend the best choice for a collaborative, growth-oriented candidate. "
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"Include a brief breakdown per option and a clear recommendation."
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)
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# ── Layout ─────────────────────────────────────────────────────────────────────
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left_col, right_col = st.columns([1, 1], gap="large")
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with left_col:
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survey_name = st.text_input(
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"Survey name (optional)",
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placeholder="e.g. Culture Fit Round 1",
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key="survey_name",
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)
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mode = st.radio("Mode", ["Quick", "Detailed"], horizontal=True, key="survey_mode")
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st.caption(
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"**Quick** — best answer + one-liner per question | "
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"**Detailed** — option-by-option breakdown"
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)
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# Input tabs
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if vision_up:
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tab_text, tab_screenshot = st.tabs(["📝 Paste Text", "🖼️ Screenshot"])
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else:
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st.info(
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"📷 Screenshot input unavailable — vision service not running. \n"
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"Start it with: `bash scripts/manage-vision.sh start`"
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)
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tab_text = st.container()
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tab_screenshot = None
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image_b64: str | None = None
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raw_text: str = ""
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with tab_text:
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raw_text = st.text_area(
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"Paste survey questions here",
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height=280,
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placeholder=(
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"Q1: Which describes your ideal work environment?\n"
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"A. Solo focused work\nB. Collaborative team\n"
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"C. Mix of both\nD. Depends on the task"
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),
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key="survey_text",
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)
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if tab_screenshot is not None:
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with tab_screenshot:
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st.caption("Paste from clipboard or upload a screenshot file.")
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paste_col, upload_col = st.columns(2)
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with paste_col:
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try:
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from streamlit_paste_button import paste_image_button
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paste_result = paste_image_button("📋 Paste from clipboard", key="paste_btn")
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if paste_result and paste_result.image_data:
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buf = io.BytesIO()
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paste_result.image_data.save(buf, format="PNG")
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image_b64 = base64.b64encode(buf.getvalue()).decode()
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st.image(
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paste_result.image_data,
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caption="Pasted image",
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use_container_width=True,
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)
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except ImportError:
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st.warning("streamlit-paste-button not installed. Use file upload.")
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with upload_col:
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uploaded = st.file_uploader(
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"Upload screenshot",
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type=["png", "jpg", "jpeg"],
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key="survey_upload",
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label_visibility="collapsed",
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)
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if uploaded:
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image_b64 = base64.b64encode(uploaded.read()).decode()
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st.image(uploaded, caption="Uploaded image", use_container_width=True)
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# Analyze button
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has_input = bool(raw_text.strip()) or bool(image_b64)
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if st.button("🔍 Analyze", type="primary", disabled=not has_input, use_container_width=True):
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with st.spinner("Analyzing…"):
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try:
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router = LLMRouter()
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if image_b64:
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prompt = _build_image_prompt(mode)
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output = router.complete(
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prompt,
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images=[image_b64],
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fallback_order=router.config.get("vision_fallback_order"),
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)
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source = "screenshot"
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else:
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prompt = _build_text_prompt(raw_text, mode)
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output = router.complete(
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prompt,
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system=_SURVEY_SYSTEM,
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fallback_order=router.config.get("research_fallback_order"),
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)
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source = "text_paste"
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st.session_state["survey_output"] = output
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st.session_state["survey_source"] = source
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st.session_state["survey_image_b64"] = image_b64
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st.session_state["survey_raw_text"] = raw_text
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except Exception as e:
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st.error(f"Analysis failed: {e}")
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with right_col:
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output = st.session_state.get("survey_output")
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if output:
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st.markdown("### Analysis")
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st.markdown(output)
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st.divider()
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with st.form("save_survey_form"):
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reported_score = st.text_input(
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"Reported score (optional)",
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placeholder="e.g. 82% or 4.2/5",
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key="reported_score_input",
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)
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if st.form_submit_button("💾 Save to Job"):
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source = st.session_state.get("survey_source", "text_paste")
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image_b64_saved = st.session_state.get("survey_image_b64")
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raw_text_saved = st.session_state.get("survey_raw_text", "")
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image_path = ""
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if image_b64_saved:
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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save_dir = (
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Path(__file__).parent.parent.parent
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/ "data"
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/ "survey_screenshots"
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/ str(selected_job_id)
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)
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save_dir.mkdir(parents=True, exist_ok=True)
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img_file = save_dir / f"{ts}.png"
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img_file.write_bytes(base64.b64decode(image_b64_saved))
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image_path = str(img_file)
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insert_survey_response(
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get_db_path(),
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job_id=selected_job_id,
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survey_name=survey_name,
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source=source,
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raw_input=raw_text_saved,
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image_path=image_path,
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mode=mode.lower(),
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llm_output=output,
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reported_score=reported_score,
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)
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st.success("Saved!")
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del st.session_state["survey_output"]
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st.rerun()
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else:
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st.markdown("### Analysis")
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st.caption("Results will appear here after analysis.")
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# ── History ────────────────────────────────────────────────────────────────────
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st.divider()
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st.subheader("📂 Response History")
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history = get_survey_responses(get_db_path(), job_id=selected_job_id)
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if not history:
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st.caption("No saved responses for this job yet.")
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else:
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for resp in history:
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label = resp.get("survey_name") or "Survey response"
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ts = (resp.get("created_at") or "")[:16]
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score = resp.get("reported_score")
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score_str = f" · Score: {score}" if score else ""
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with st.expander(f"{label} · {ts}{score_str}"):
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st.caption(f"Mode: {resp.get('mode', '?')} · Source: {resp.get('source', '?')}")
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if resp.get("raw_input"):
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with st.expander("Original input"):
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st.text(resp["raw_input"])
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st.markdown(resp.get("llm_output", ""))
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