Update main.py

main.py

@@ -5,23 +5,25 @@ from fastapi.responses import StreamingResponse
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 import os
-os.environ["TRANSFORMERS_CACHE"] = "/app/hf_cache"
+import asyncio
 
+# ✅ Set a safe and writable HF cache directory
+os.environ["HF_HOME"] = "./hf_home"
+os.makedirs(os.environ["HF_HOME"], exist_ok=True)
 
-
-# Load Qwen model and tokenizer (once)
+# ✅ Model and tokenizer (only loaded once)
 model_name = "Qwen/Qwen2.5-0.5B-Instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
 
-# Set device
-device = torch.device("cpu")  # Or "cuda" if using GPU
+# ✅ Set device (use GPU if available)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-# FastAPI app
+# ✅ FastAPI app
 app = FastAPI()
 
-# CORS settings
+# ✅ CORS settings
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -30,26 +32,25 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Request body model
+# ✅ Request schema
 class Question(BaseModel):
     question: str
 
-# System prompt (your custom instructions)
+# ✅ System prompt
 SYSTEM_PROMPT = "You are Orion, an intelligent AI assistant created by Abdullah Ali, a 13-year-old from Lahore. Respond kindly and wisely."
 
-# Chat response generator
+# ✅ Streaming generator
 async def generate_response_chunks(prompt: str):
-    # Build prompt using Qwen's expected format
     qwen_prompt = (
         f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
         f"<|im_start|>user\n{prompt}<|im_end|>\n"
         f"<|im_start|>assistant\n"
     )
 
-    # Tokenize input
+    # Tokenize prompt
     inputs = tokenizer(qwen_prompt, return_tensors="pt").to(device)
 
-    # Generate response
+    # Generate output
     outputs = model.generate(
         **inputs,
         max_new_tokens=256,
@@ -59,16 +60,16 @@ async def generate_response_chunks(prompt: str):
         pad_token_id=tokenizer.eos_token_id
     )
 
-    # Decode and yield line by line
+    # Decode output
     full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
     reply = full_output.split("<|im_start|>assistant\n")[-1].strip()
 
-    for chunk in reply.split():
-        yield chunk + " "
+    # Yield chunks word by word (simulating stream)
+    for word in reply.split():
+        yield word + " "
+        await asyncio.sleep(0.01)  # slight delay for streaming effect
 
+# ✅ POST endpoint
 @app.post("/ask")
 async def ask(question: Question):
-    return StreamingResponse(
-        generate_response_chunks(question.question),
-        media_type="text/plain"
-    )
+    return StreamingResponse(generate_response_chunks(question.question), media_type="text/plain")