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main.py

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+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import JSONResponse
+from enum import Enum
+from transformers import pipeline, MarianMTModel, MarianTokenizer
+import shutil
+import os
+import uuid
+from googletrans import Translator
+
+app = FastAPI()
+
+# 🌍 Language Enum for dropdown in Swagger
+class LanguageEnum(str, Enum):
+    ta = "ta"  # Tamil
+    fr = "fr"  # French
+    es = "es"  # Spanish
+    de = "de"  # German
+    it = "it"  # Italian
+    hi = "hi"  # Hindi
+    ru = "ru"  # Russian
+    zh = "zh"  # Chinese
+    ar = "ar"  # Arabic
+
+# 🌐 Map target language to translation model
+model_map = {
+    "fr": "Helsinki-NLP/opus-mt-en-fr",
+    "es": "Helsinki-NLP/opus-mt-en-es",
+    "de": "Helsinki-NLP/opus-mt-en-de",
+    "it": "Helsinki-NLP/opus-mt-en-it",
+    "hi": "Helsinki-NLP/opus-mt-en-hi",
+    "ru": "Helsinki-NLP/opus-mt-en-ru",
+    "zh": "Helsinki-NLP/opus-mt-en-zh",
+    "ar": "Helsinki-NLP/opus-mt-en-ar",
+    "ta": "gsarti/opus-mt-en-ta"
+}
+
+def translate_text(text, target_lang):
+    if target_lang == "ta":
+        try:
+            translator = Translator()
+            result = translator.translate(text, dest="ta")
+            return result.text
+        except Exception as e:
+            return f"Google Translate failed: {str(e)}"
+
+    if target_lang not in model_map:
+        return f"No model for language: {target_lang}"
+
+    model_name = model_map[target_lang]
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    model = MarianMTModel.from_pretrained(model_name)
+    encoded = tokenizer([text], return_tensors="pt", padding=True)
+    translated = model.generate(**encoded)
+    return tokenizer.batch_decode(translated, skip_special_tokens=True)[0]
+
+# 🧠 Generate a random English sentence
+def generate_random_sentence(prompt="Daily conversation", max_length=30):
+    generator = pipeline("text-generation", model="distilgpt2")
+    result = generator(prompt, max_length=max_length, num_return_sequences=1)
+    return result[0]["generated_text"].strip()
+
+# 🎤 Transcription endpoint
+@app.post("/transcribe")
+async def transcribe(audio: UploadFile = File(...)):
+    temp_filename = f"temp_{uuid.uuid4().hex}.wav"
+    with open(temp_filename, "wb") as f:
+        shutil.copyfileobj(audio.file, f)
+    try:
+        asr = pipeline("automatic-speech-recognition", model="openai/whisper-medium")
+        result = asr(temp_filename)
+        return JSONResponse(content={"transcribed_text": result["text"]})
+    finally:
+        os.remove(temp_filename)
+
+# 🌍 Translation endpoint
+@app.post("/translate")
+async def translate(text: str = Form(...), target_lang: LanguageEnum = Form(...)):
+    translated = translate_text(text, target_lang.value)
+    return JSONResponse(content={"translated_text": translated})
+
+# 🔁 Combined endpoint (speech-to-translation)
+@app.post("/process")
+async def process(audio: UploadFile = File(...), target_lang: LanguageEnum = Form(...)):
+    temp_filename = f"temp_{uuid.uuid4().hex}.wav"
+    with open(temp_filename, "wb") as f:
+        shutil.copyfileobj(audio.file, f)
+    try:
+        asr = pipeline("automatic-speech-recognition", model="openai/whisper-medium")
+        result = asr(temp_filename)
+        transcribed_text = result["text"]
+        translated_text = translate_text(transcribed_text, target_lang.value)
+        return JSONResponse(content={
+            "transcribed_text": transcribed_text,
+            "translated_text": translated_text
+        })
+    finally:
+        os.remove(temp_filename)
+
+# ✨ Generate + Translate endpoint
+@app.get("/generate")
+def generate(prompt: str = "Daily conversation", target_lang: LanguageEnum = LanguageEnum.it):
+    english = generate_random_sentence(prompt)
+    translated = translate_text(english, target_lang.value)
+    return {
+        "prompt": prompt,
+        "english": english,
+        "translated": translated
+    }

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch
+googletrans==4.0.0-rc1
+python-multipart
+streamlit
+requests
+streamlit-webrtc
+av
+aiohttp
+numpy
+