Create app.py

app.py

@@ -0,0 +1,106 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Tuple
+from huggingface_hub import InferenceClient
+import edge_tts
+import tempfile
+import asyncio
+import os
+from fastapi.responses import FileResponse
+from groq import Groq
+
+app = FastAPI()
+
+# Initialize the client for Hugging Face Inference API
+# client = InferenceClient("unsloth/gemma-2b-it-bnb-4bit")
+client = Groq(
+    api_key='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'
+)
+
+async def text_to_speech(text, voice, rate, pitch):
+    voice_short_name = voice.split(" - ")[0]
+    rate_str = f"{rate:+d}%"
+    pitch_str = f"{pitch:+d}Hz"
+    communicate = edge_tts.Communicate(text, voice_short_name, rate=rate_str, pitch=pitch_str)
+    submaker = edge_tts.SubMaker()
+    
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+        tmp_path = tmp_file.name
+
+        async for chunk in communicate.stream():
+            if chunk["type"] == "audio":
+                tmp_file.write(chunk["data"])
+            elif chunk["type"] == "WordBoundary":
+                submaker.create_sub((chunk["offset"], chunk["duration"]), chunk["text"])
+
+
+    # with open('test.vtt', "w", encoding="utf-8") as file:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode='w+', encoding='utf-8') as tmp_file:
+        tmp_vtt_path = tmp_file.name
+        tmp_file.write(submaker.generate_subs())
+
+    return tmp_path, tmp_vtt_path, None
+
+def tts_interface(text, voice, rate, pitch):
+    audio, vtt, warning = asyncio.run(text_to_speech(text, voice, rate, pitch))
+    return audio, vtt, warning
+
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!"}
+
+
+# Define a model for the incoming request
+class ChatRequest(BaseModel):
+    message: str
+    history: List[Tuple[str, str]] = []
+    system_message: str
+    max_tokens: int = 512
+    temperature: float = 0.7
+    top_p: float = 0.95
+
+
+@app.get("/file/")
+def file(path: str):
+    return FileResponse(path, media_type="audio/mpeg", filename="audio.mp3")
+
+@app.get("/file-vtt/")
+def fileVtt(path: str):
+    return FileResponse(path)
+
+# Define a route to handle POST requests
+@app.post("/chat")
+def chat(request: ChatRequest):
+    messages = [{"role": "system", "content": request.system_message}]
+
+    for val in request.history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": request.message})
+
+    try:
+        response = client.chat.completions.create(
+            model="llama-3.1-8b-instant",
+            messages=messages,
+            max_tokens=request.max_tokens,
+            stream=False,
+            stop=None,
+            temperature=request.temperature,
+            top_p=request.top_p,
+        )
+
+        data = tts_interface((response.choices[0].message.content.replace('**', '')).replace('**', ''), 'en-GB-MaisieNeural - en-GB (Female)', 0, 0)
+
+        if os.path.exists(data[0]):
+            return {
+                "text": response.choices[0].message.content.replace('**', ''),
+                "audio" : data[0],
+                "vtt" : data[1]
+            }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))