Create app.py

app.py

@@ -0,0 +1,89 @@
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List, Dict, Any
+import uvicorn
+from model_loader import get_local_llm_instance
+
+app = FastAPI(title="Stateless Agent Pipeline")
+
+# Enable global cross-origin resource sharing for frontend html access
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load model engine universally on runtime startup
+try:
+    llm_instance = get_local_llm_instance()
+except Exception as init_err:
+    print(f"[CRITICAL ERROR] Failed to load local weights: {init_err}")
+    llm_instance = None
+
+# Validation structure for parsing the data packets cleanly
+class ChatPayload(BaseModel):
+    user_id: str
+    user_message: str
+    current_chat_history: List[Dict[str, Any]] = []
+    user_files: Dict[str, Any] = {}
+
+@app.get("/")
+def read_root():
+    return {"status": "online", "engine": "Llama.cpp local cluster running flawlessly"}
+
+@app.post("/chat")
+async def chat_endpoint(payload: ChatPayload):
+    global llm_instance
+    if llm_instance is None:
+        raise HTTPException(status_code=500, detail="Local LLM instance cluster is offline.")
+    
+    try:
+        user_query = payload.user_message
+        
+        # Build strict system directives for clean output responses
+        system_instruction = (
+            "<|im_start|>system\n"
+            "You are a helpful, extremely fast AI assistant. "
+            "Respond cleanly, accurately and directly to the prompt. "
+            "Keep formatting minimal.<|im_end|>\n"
+        )
+        
+        # Format chat history context string if it exists
+        history_context = ""
+        for turn in payload.current_chat_history[-4:]: # Keep only the last 4 exchanges to preserve fast RAM context
+            role = "user" if turn.get("role") == "user" else "assistant"
+            content = turn.get("content", "")
+            history_context += f"<|im_start|>{role}\n{content}<|im_end|>\n"
+            
+        # Compile complete operational template string
+        final_prompt = f"{system_instruction}{history_context}<|im_start|>user\n{user_query}<|im_end|>\n<|im_start|>assistant\n"
+        
+        # Run synchronous inference across CPU matrix
+        output = llm_instance(
+            final_prompt,
+            max_tokens=512,       # Generation constraint for faster response times
+            stop=["<|im_end|>", "<|im_start|>", "user:", "assistant:"],
+            echo=False
+        )
+        
+        generated_text = output["choices"][0]["text"].strip()
+        
+        # Re-construct updated structural array history block
+        updated_history = payload.current_chat_history + [
+            {"role": "user", "content": user_query},
+            {"role": "assistant", "content": generated_text}
+        ]
+        
+        return {
+            "updated_chat_history": updated_history,
+            "updated_files": payload.user_files
+        }
+        
+    except Exception as exec_error:
+        raise HTTPException(status_code=500, detail=f"Inference Engine Error: {str(exec_error)}")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)