Create app.py

app.py

@@ -0,0 +1,182 @@
+from fastapi import FastAPI, Request
+from fastapi.responses import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+from typing import List, Dict, Any, Optional
+from pydantic import BaseModel
+import asyncio
+import httpx
+
+from config import cookies, headers
+from prompts import ChiplingPrompts
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Define request model
+class ChatRequest(BaseModel):
+    message: str
+    messages: List[Dict[Any, Any]]
+    model: Optional[str] = "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
+
+async def generate(json_data: Dict[str, Any]):
+    max_retries = 5
+    for attempt in range(max_retries):
+        async with httpx.AsyncClient(timeout=None) as client:
+            try:
+                request_ctx = client.stream(
+                    "POST",
+                    "https://api.together.ai/inference",
+                    cookies=cookies,
+                    headers=headers,
+                    json=json_data
+                )
+
+                async with request_ctx as response:
+                    if response.status_code == 200:
+                        async for line in response.aiter_lines():
+                            if line:
+                                yield f"{line}\n"
+                        return
+                    elif response.status_code == 429:
+                        if attempt < max_retries - 1:
+                            await asyncio.sleep(0.5)
+                            continue
+                        yield "data: [Rate limited, max retries]\n\n"
+                        return
+                    else:
+                        yield f"data: [Unexpected status code: {response.status_code}]\n\n"
+                        return
+            except Exception as e:
+                yield f"data: [Connection error: {str(e)}]\n\n"
+                return
+
+    yield "data: [Max retries reached]\n\n"
+
+@app.get("/")
+async def index():
+    return {"status": "ok"}
+
+@app.post("/chat")
+async def chat(request: ChatRequest):
+    current_messages = request.messages.copy()
+    
+    # Handle both single text or list content
+    if request.messages and isinstance(request.messages[-1].get('content'), list):
+        current_messages = request.messages
+    else:
+        current_messages.append({
+            'content': [{
+                'type': 'text',
+                'text': request.message
+            }],
+            'role': 'user'
+        })
+
+    json_data = {
+        'model': request.model,
+        'max_tokens': None,
+        'temperature': 0.7,
+        'top_p': 0.7,
+        'top_k': 50,
+        'repetition_penalty': 1,
+        'stream_tokens': True,
+        'stop': ['<|eot_id|>', '<|eom_id|>'],
+        'messages': current_messages,
+        'stream': True,
+    }
+
+    return StreamingResponse(generate(json_data), media_type='text/event-stream')
+
+
+@app.post("/generate-modules")
+async def generate_modules(request: Request):
+    data = await request.json()
+    search_query = data.get("searchQuery")
+
+    if not search_query:
+        return {"error": "searchQuery is required"}
+
+    system_prompt = ChiplingPrompts.generateModules(search_query)
+
+    current_messages = [
+        {
+            'role': 'system',
+            'content': [{
+                'type': 'text',
+                'text': system_prompt
+            }]
+        },
+        {
+            'role': 'user',
+            'content': [{
+                'type': 'text',
+                'text': search_query
+            }]
+        }
+    ]
+
+    json_data = {
+        'model': "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+        'max_tokens': None,
+        'temperature': 0.7,
+        'top_p': 0.7,
+        'top_k': 50,
+        'repetition_penalty': 1,
+        'stream_tokens': True,
+        'stop': ['<|eot_id|>', '<|eom_id|>'],
+        'messages': current_messages,
+        'stream': True,
+    }
+
+    return StreamingResponse(generate(json_data), media_type='text/event-stream')
+
+
+@app.post("/generate-topics")
+async def generate_topics(request: Request):
+    data = await request.json()
+    search_query = data.get("searchQuery")
+
+    if not search_query:
+        return {"error": "searchQuery is required"}
+
+    system_prompt = ChiplingPrompts.generateTopics(search_query)
+
+    current_messages = [
+        {
+            'role': 'system',
+            'content': [{
+                'type': 'text',
+                'text': system_prompt
+            }]
+        },
+        {
+            'role': 'user',
+            'content': [{
+                'type': 'text',
+                'text': search_query
+            }]
+        }
+    ]
+
+    json_data = {
+        'model': "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+        'max_tokens': None,
+        'temperature': 0.7,
+        'top_p': 0.7,
+        'top_k': 50,
+        'repetition_penalty': 1,
+        'stream_tokens': True,
+        'stop': ['<|eot_id|>', '<|eom_id|>'],
+        'messages': current_messages,
+        'stream': True,
+    }
+
+    return StreamingResponse(generate(json_data), media_type='text/event-stream')