creating the main engine

app.py

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+from fastapi import FastAPI, Form
+from fastapi.responses import FileResponse, JSONResponse
+from pydantic import BaseModel
+import torch
+from transformers import AutoProcessor, BarkModel, pipeline
+import scipy.io.wavfile as wavfile
+import uuid
+import os
+from typing import Optional
+
+# Load TTS model and processor
+processor = AutoProcessor.from_pretrained("suno/bark")
+model = BarkModel.from_pretrained("suno/bark")
+
+# Load sentiment analysis pipeline (using multilingual model)
+sentiment_model = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+# Ensure model is on CPU or CUDA if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# FastAPI app
+app = FastAPI()
+
+# Endpoint input models
+class TTSRequest(BaseModel):
+    text: str
+
+class SentimentRequest(BaseModel):
+    text: str
+
+class LegalDocRequest(BaseModel):
+    text: str
+    domain: Optional[str] = "general"
+
+@app.get("/")
+def root():
+    return {"message": "Welcome to Kinyarwanda NLP API"}
+
+@app.post("/tts/")
+def text_to_speech(request: TTSRequest):
+    try:
+        # Generate speech
+        inputs = processor(request.text, return_tensors="pt")
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        speech = model.generate(**inputs)
+
+        # Save audio
+        output_file = f"output_{uuid.uuid4().hex}.wav"
+        speech_np = speech.cpu().numpy().squeeze()
+        wavfile.write(output_file, rate=22050, data=speech_np)
+
+        return FileResponse(output_file, media_type="audio/wav")
+
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+@app.post("/sentiment/")
+def analyze_sentiment(request: SentimentRequest):
+    try:
+        result = sentiment_model(request.text)
+        return {"result": result}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+@app.post("/legal-parse/")
+def parse_legal_document(request: LegalDocRequest):
+    try:
+        # Placeholder logic (replace with training-based custom logic)
+        keywords = ["contract", "agreement", "party", "terms"]
+        found_keywords = [kw for kw in keywords if kw in request.text.lower()]
+        return {"identified_keywords": found_keywords, "domain": request.domain}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})