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samples/ai_recipe_generation/lib/services/gemini.dart

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1.8 KiB

import 'package:google_generative_ai/google_generative_ai.dart';
import '../features/prompt/prompt_model.dart';
class GeminiService {
static Future<GenerateContentResponse> generateContent(
GenerativeModel model, PromptData prompt) async {
if (prompt.images.isEmpty) {
return await GeminiService.generateContentFromText(model, prompt);
} else {
return await GeminiService.generateContentFromMultiModal(model, prompt);
}
}
static Future<GenerateContentResponse> generateContentFromMultiModal(
GenerativeModel model, PromptData prompt) async {
final mainText = TextPart(prompt.textInput);
final additionalTextParts =
prompt.additionalTextInputs.map((t) => TextPart(t));
final imagesParts = <DataPart>[];
for (var f in prompt.images) {
final bytes = await (f.readAsBytes());
imagesParts.add(DataPart('image/jpeg', bytes));
}
final input = [
Content.multi([...imagesParts, mainText, ...additionalTextParts])
];
return await model.generateContent(
input,
generationConfig: GenerationConfig(
temperature: 0.4,
topK: 32,
topP: 1,
maxOutputTokens: 4096,
),
safetySettings: [
SafetySetting(HarmCategory.harassment, HarmBlockThreshold.high),
SafetySetting(HarmCategory.hateSpeech, HarmBlockThreshold.high),
],
);
}
static Future<GenerateContentResponse> generateContentFromText(
GenerativeModel model, PromptData prompt) async {
final mainText = TextPart(prompt.textInput);
final additionalTextParts =
prompt.additionalTextInputs.map((t) => TextPart(t)).join("\n");
return await model.generateContent([
Content.text(
'${mainText.text} \n $additionalTextParts',
)
]);
}
}