Spaces:
Runtime error
Runtime error
File size: 4,036 Bytes
1278df1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | """Streamlit frontend for the multi-domain analyzer platform."""
from __future__ import annotations
import streamlit as st
from app.examples import EXAMPLES
from schemas.request import AnalyzeCodeRequest
from services.analysis_service import AnalysisService
analysis_service = AnalysisService()
def _analyze(code: str, context_window: str, traceback_text: str, domain_hint: str):
"""Run the analysis service with validated request payloads."""
request = AnalyzeCodeRequest(
code=code,
context_window=context_window,
traceback_text=traceback_text,
domain_hint=domain_hint, # type: ignore[arg-type]
)
return analysis_service.analyze(request)
def main() -> None:
"""Render the Streamlit UI."""
st.set_page_config(page_title="Multi-Domain AI Code Analyzer", layout="wide")
st.title("Multi-Domain AI Code Analyzer & Improvement System")
st.caption("PyTorch-powered code review across DSA, Data Science, ML/DL, and Web backend code.")
example_name = st.selectbox("Example input", list(EXAMPLES.keys()))
example = EXAMPLES[example_name]
auto_analyze = st.toggle("Real-time scoring", value=True)
left, right = st.columns([1.2, 1.0])
with left:
code = st.text_area("Code input", value=example["code"], height=420)
context_window = st.text_area("Context window", value=example["context_window"], height=100)
traceback_text = st.text_area("Optional traceback / runtime hint", value=example["traceback_text"], height=100)
domain_hint = st.selectbox("Domain hint", ["auto", "dsa", "data_science", "ml_dl", "web"], index=["auto", "dsa", "data_science", "ml_dl", "web"].index(example["domain_hint"]))
analyze_clicked = st.button("Analyze Code", type="primary")
result = None
if code and (analyze_clicked or auto_analyze):
result = _analyze(code, context_window, traceback_text, domain_hint)
with right:
if result is None:
st.info("Paste code or load an example to start analysis.")
else:
metric_cols = st.columns(4)
metric_cols[0].metric("Detected domain", result.detected_domain)
metric_cols[1].metric("ML score", f"{result.score_breakdown.ml_score:.0%}")
metric_cols[2].metric("Domain score", f"{result.score_breakdown.domain_score:.0%}")
metric_cols[3].metric("Reward", f"{result.score_breakdown.reward:.0%}")
st.bar_chart(result.domain_confidences)
st.caption(result.summary)
if result is not None:
overview_tab, suggestions_tab, domain_tab, static_tab = st.tabs(
["Overview", "Suggestions", "Domain Detail", "Static Analysis"]
)
with overview_tab:
st.subheader("Improvement Plan")
for step in result.improvement_plan:
st.write(f"- {step}")
st.subheader("Complexity")
st.write(
{
"time_complexity": result.static_analysis.time_complexity,
"space_complexity": result.static_analysis.space_complexity,
"cyclomatic_complexity": result.static_analysis.cyclomatic_complexity,
}
)
with suggestions_tab:
st.subheader("Suggestions")
for suggestion in result.domain_analysis.suggestions:
st.write(f"- {suggestion}")
if result.domain_analysis.issues:
st.subheader("Issues")
for issue in result.domain_analysis.issues:
st.write(f"- [{issue.severity}] {issue.title}: {issue.description}")
with domain_tab:
st.subheader("Domain Highlights")
st.json(result.domain_analysis.highlights)
st.write(f"Domain score: {result.domain_analysis.domain_score:.0%}")
with static_tab:
st.subheader("Static Analysis")
st.json(result.static_analysis.model_dump())
if __name__ == "__main__":
main()
|