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import React, { useState } from 'react';
import {
Paper,
Typography,
Box,
TextField,
Button,
CircularProgress,
Divider
} from '@material-ui/core';
import { makeStyles } from '@material-ui/core/styles';
const useStyles = makeStyles((theme) => ({
paper: {
padding: theme.spacing(2),
marginTop: theme.spacing(2)
},
marginBottom: {
marginBottom: theme.spacing(2)
},
dividerMargin: {
margin: `${theme.spacing(2)}px 0`
},
responseBox: {
padding: theme.spacing(2),
backgroundColor: '#f5f5f5',
borderRadius: theme.shape.borderRadius,
marginTop: theme.spacing(2),
whiteSpace: 'pre-wrap'
},
buttonProgress: {
marginLeft: theme.spacing(1)
}
}));
const LlmAnalysis = ({ visionResults, model }) => {
const classes = useStyles();
const [userQuery, setUserQuery] = useState('');
const [isAnalyzing, setIsAnalyzing] = useState(false);
const [analysisResult, setAnalysisResult] = useState(null);
const [error, setError] = useState(null);
// Format time for display
const formatTime = (ms) => {
if (ms === undefined || ms === null || isNaN(ms)) return '-';
const num = Number(ms);
if (num < 1000) return `${num.toFixed(2)} ms`;
return `${(num / 1000).toFixed(2)} s`;
};
const handleAnalyze = async () => {
if (!userQuery.trim()) return;
setIsAnalyzing(true);
setError(null);
try {
const response = await fetch('/api/analyze', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
visionResults: visionResults,
userQuery: userQuery
}),
});
if (!response.ok) {
throw new Error(`HTTP error! Status: ${response.status}`);
}
const data = await response.json();
if (data.error) {
setError(data.error);
} else {
setAnalysisResult(data);
}
} catch (err) {
console.error('Error analyzing with LLM:', err);
setError(`Error analyzing with LLM: ${err.message}`);
} finally {
setIsAnalyzing(false);
}
};
if (!visionResults) return null;
return (
<Paper className={classes.paper}>
<Typography variant="h6" gutterBottom>
Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results
</Typography>
<Typography variant="body2" className={classes.marginBottom}>
Ask a question about the detected objects or classifications to get an AI-powered analysis.
</Typography>
<TextField
fullWidth
label="Your question about the image"
variant="outlined"
value={userQuery}
onChange={(e) => setUserQuery(e.target.value)}
disabled={isAnalyzing}
className={classes.marginBottom}
placeholder={model === 'vit'
? "E.g., What category does this image belong to?"
: "E.g., How many people are in this image?"}
/>
<Button
variant="contained"
color="primary"
onClick={handleAnalyze}
disabled={isAnalyzing || !userQuery.trim()}
>
Analyze with AI
{isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}
</Button>
{error && (
<Box mt={2}>
<Typography color="error">{error}</Typography>
</Box>
)}
{analysisResult && (
<>
<Divider className={classes.dividerMargin} />
<Typography variant="subtitle1" gutterBottom>
AI Analysis:
</Typography>
<Box className={classes.responseBox}>
<Typography variant="body1">
{analysisResult.response}
</Typography>
</Box>
{analysisResult.performance && (
<Box mt={1}>
<Typography variant="body2" color="textSecondary">
Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}
</Typography>
</Box>
)}
</>
)}
</Paper>
);
};
export default LlmAnalysis;
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