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import dotenv from 'dotenv';
dotenv.config();
class HuggingFaceService {
constructor() {
this.hf = new HfInference(process.env.HUGGINGFACE_API_KEY);
this.model = 'mistralai/Mistral-7B-Instruct-v0.2';
}
/**
* Analyze training data using Hugging Face
*/
async analyzeTrainingData(documentText, rawData = null) {
try {
const prompt = `You are an expert data analyst for the National Disaster Management Authority (NDMA) of India.
Analyze the following disaster management training data and provide comprehensive insights in JSON format:
${documentText.substring(0, 3000)}
Provide the following in valid JSON format:
{
"totalTrainings": <number>,
"totalParticipants": <number>,
"themeDistribution": {"earthquake": <count>, "flood": <count>, etc},
"stateWiseCoverage": {"state": <count>},
"averageCompletionRate": "<percentage>%",
"gapAnalysis": {
"underservedStates": ["state1", "state2"],
"underservedThemes": ["theme1"],
"criticalGaps": ["gap1", "gap2"]
},
"recommendations": ["rec1", "rec2", "rec3"],
"keyInsights": ["insight1", "insight2", "insight3"]
}
Respond ONLY with valid JSON, no markdown or explanations.`;
const response = await this.hf.textGeneration({
model: this.model,
inputs: prompt,
parameters: {
max_new_tokens: 1000,
temperature: 0.7,
return_full_text: false
}
});
let analysisText = response.generated_text.trim();
// Clean up response
analysisText = analysisText.replace(/```json\n?/g, '').replace(/```\n?/g, '').trim();
// Try to parse JSON
let analysis;
try {
analysis = JSON.parse(analysisText);
} catch (parseError) {
console.warn('JSON parsing failed, creating fallback analysis');
analysis = this.createFallbackAnalysis(documentText, rawData);
}
return analysis;
} catch (error) {
console.error('Hugging Face analysis error:', error);
throw new Error(`Analysis failed: ${error.message}`);
}
}
/**
* Generate executive summary
*/
async generateExecutiveSummary(analysisResults) {
try {
const prompt = `Based on this disaster management training analysis, write a concise 3-paragraph executive summary for NDMA officials:
${JSON.stringify(analysisResults, null, 2)}
Write in formal, professional language suitable for government documentation.`;
const response = await this.hf.textGeneration({
model: this.model,
inputs: prompt,
parameters: {
max_new_tokens: 500,
temperature: 0.7,
return_full_text: false
}
});
return response.generated_text.trim();
} catch (error) {
throw new Error(`Executive summary generation failed: ${error.message}`);
}
}
/**
* RAG-based question answering
*/
async answerQuestion(question, context) {
try {
const prompt = `Context: ${context.substring(0, 2000)}
Question: ${question}
Provide a detailed, data-driven answer based on the context:`;
const response = await this.hf.textGeneration({
model: this.model,
inputs: prompt,
parameters: {
max_new_tokens: 300,
temperature: 0.7,
return_full_text: false
}
});
return response.generated_text.trim();
} catch (error) {
throw new Error(`Question answering failed: ${error.message}`);
}
}
/**
* Fallback analysis if AI fails
*/
createFallbackAnalysis(documentText, rawData) {
const analysis = {
totalTrainings: 0,
totalParticipants: 0,
themeDistribution: {},
stateWiseCoverage: {},
averageCompletionRate: "N/A",
gapAnalysis: {
underservedStates: [],
underservedThemes: [],
criticalGaps: ["Unable to perform detailed analysis - please check data format"]
},
recommendations: [
"Ensure data is in proper format with clear headers",
"Include all required fields: training ID, date, location, theme, participants",
"Maintain consistent data entry standards"
],
keyInsights: [
"Data analysis completed - please review the uploaded document for details"
]
};
if (rawData && Array.isArray(rawData)) {
analysis.totalTrainings = rawData.length;
rawData.forEach(row => {
const participantFields = ['participants', 'Participants', 'total_participants'];
for (const field of participantFields) {
if (row[field]) {
analysis.totalParticipants += parseInt(row[field]) || 0;
break;
}
}
});
}
return analysis;
}
}
export default new HuggingFaceService();
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