Create routers/llm_chat.py

routers/llm_chat.py

@@ -0,0 +1,51 @@
+# routers/llm_chat.py
+from fastapi import APIRouter, HTTPException, Header
+import requests
+import os
+from pydantic import BaseModel
+
+router = APIRouter(
+    prefix="/api/v1",  # Prefix for all routes in this router
+    tags=["LLM Chat"],  # Tag for OpenAPI documentation
+)
+
+# Load environment variables
+LLM_API_URL = os.getenv("LLM_API_URL", "https://pvanand-audio-chat.hf.space/llm-agent")
+API_KEY = os.getenv("API_KEY", "44d5c")
+
+# Pydantic model for request validation
+class LLMChatRequest(BaseModel):
+    prompt: str
+    system_message: str = ""
+    model_id: str = "openai/gpt-4o-mini"
+    conversation_id: str = "string"
+    user_id: str = "string"
+
+@router.post("/llm-chat", summary="Send a prompt to the LLM", description="This endpoint sends a prompt to the LLM and returns the response.")
+async def llm_chat(
+    request: LLMChatRequest,
+    x_api_key: str = Header(None, description="API Key for authentication")
+):
+    if x_api_key != API_KEY:
+        raise HTTPException(status_code=403, detail="Invalid API Key")
+
+    payload = {
+        "prompt": request.prompt,
+        "system_message": request.system_message,
+        "model_id": request.model_id,
+        "conversation_id": request.conversation_id,
+        "user_id": request.user_id
+    }
+
+    headers = {
+        "accept": "application/json",
+        "X-API-Key": x_api_key,
+        "Content-Type": "application/json"
+    }
+
+    # Use requests to call the external API
+    response = requests.post(LLM_API_URL, json=payload, headers=headers)
+    if response.status_code != 200:
+        raise HTTPException(status_code=response.status_code, detail="Error from LLM API")
+
+    return response.json()