Update app.py

app.py

@@ -1,4 +1,116 @@
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+import aiohttp
+import json
+import time
+import random
+import ast
+import urllib.parse
+from apscheduler.schedulers.background import BackgroundScheduler
 import os
-from groq import Groq
+from pydantic import BaseModel
 
-exec(os.environ.get('MainData'))
+SAMBA_NOVA_API_KEY = os.environ.get("SAMBA_NOVA_API_KEY", None)
+
+app = FastAPI()
+
+# Time-Limited Infinite Cache
+cache = {}
+CACHE_DURATION = 120
+
+# Function to clean up expired cache entries
+def cleanup_cache():
+    current_time = time.time()
+    for key, (value, timestamp) in list(cache.items()):
+        if current_time - timestamp > CACHE_DURATION:
+            del cache[key]
+
+# Initialize and start the scheduler
+scheduler = BackgroundScheduler()
+scheduler.add_job(cleanup_cache, 'interval', seconds=60)  # Run cleanup every 60 seconds
+scheduler.start()
+
+class StreamTextRequest(BaseModel):
+    query: str
+    history: str = "[]"
+    model: str = "llama3-8b"
+    api_key: str = None
+
+@app.post("/stream_text")
+async def stream_text(request: StreamTextRequest):
+    current_time = time.time()
+    cache_key = (request.query, request.history, request.model)
+
+    # Check if the request is in the cache and not expired
+    if cache_key in cache:
+        cached_response, timestamp = cache[cache_key]
+        return StreamingResponse(iter([f"{cached_response}"]), media_type='text/event-stream')
+
+    # Model selection logic
+    if "405" in request.model:
+        fmodel = "Meta-Llama-3.1-405B-Instruct"
+    if "70" in request.model:
+        fmodel = "Meta-Llama-3.1-70B-Instruct"
+    else:
+        fmodel = "Meta-Llama-3.1-8B-Instruct"
+
+    system_message = """You are Voicee, a friendly and intelligent voice assistant created by KingNish. Your primary goal is to provide accurate, concise, and engaging responses while maintaining a positive and upbeat tone. Always aim to provide clear and relevant information that directly addresses the user's query, but feel free to sprinkle in a dash of humor—after all, laughter is the best app! Keep your responses brief and to the point, avoiding unnecessary details or tangents, unless they’re hilariously relevant. Use a friendly and approachable tone to create a pleasant interaction, and don’t shy away from a cheeky pun or two! Tailor your responses based on the user's input and previous interactions, ensuring a personalized experience that feels like chatting with a witty friend. Invite users to ask follow-up questions or clarify their needs, fostering a conversational flow that’s as smooth as butter on a hot pancake. Aim to put a smile on the user's face with light-hearted and fun responses, and be proactive in offering additional help or suggestions related to the user's query. Remember, your goal is to be the go-to assistant for users, making their experience enjoyable and informative—like a delightful dessert after a hearty meal!"""
+
+    messages = [{'role': 'system', 'content': system_message}]
+
+    messages.extend(ast.literal_eval(request.history))
+
+    messages.append({'role': 'user', 'content': request.query})
+    
+    data = {'messages': messages, 'stream': True, 'model': fmodel}
+
+    api_key = request.api_key or SAMBA_NOVA_API_KEY
+
+    async def stream_response():
+        async with aiohttp.ClientSession() as session:
+            async with session.post('https://api.sambanova.ai/v1/chat/completions', headers = { 'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json' }, json=data) as response:
+                if response.status != 200:
+                    raise HTTPException(status_code=response.status, detail="Error fetching AI response")
+
+                response_content = ""
+                async for line in response.content:
+                    line = line.decode('utf-8').strip()
+                    if line.startswith('data: {'):
+                        json_data = line[6:]
+                        try:
+                            parsed_data = json.loads(json_data)
+                            content = parsed_data.get("choices", [{}])[0].get("delta", {}).get("content", '')
+                            if content:
+                                content = content.replace("\n", " ")
+                                response_content += f"data: {content}\n\n"
+                                yield f"data: {content}\n\n"
+                        except json.JSONDecodeError as e:
+                            print(f"Error decoding JSON: {e}")
+                            yield f"data: Error decoding JSON\n\n"
+
+                # Cache the full response
+                cache[cache_key] = (response_content, current_time)
+
+    return StreamingResponse(stream_response(), media_type='text/event-stream')
+
+
+
+# Serve index.html from the same directory as your main.py file 
+from starlette.responses import FileResponse 
+
+@app.get("/script1.js")
+async def script1_js():
+    return FileResponse("script1.js")
+
+@app.get("/script2.js")
+async def script2_js():
+    return FileResponse("script2.js")
+
+@app.get("/")
+async def read_index():
+    return FileResponse('index.html')
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7068, reload=True)