Create main.py

main.py

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+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import asyncio
+
+# FastAPI app
+app = FastAPI()
+
+# CORS Middleware (so JS from browser can access it)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Change "*" to your frontend URL for better security
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Request body model
+class Question(BaseModel):
+    question: str
+
+# Load the model and tokenizer
+model_name = "Qwen/Qwen2.5-7B-Instruct"  # Use Qwen2.5-7B-Instruct (adjust for VL if needed)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,  # Use float16 for GPU memory efficiency
+    device_map="auto",  # Automatically map to GPU/CPU
+    trust_remote_code=True
+)
+
+async def generate_response_chunks(prompt: str):
+    try:
+        # Prepare the input prompt
+        messages = [
+            {"role": "system", "content": "You are Orion AI assistant created by Abdullah Ali, who is very intelligent, 13 years old, and lives in Lahore."},
+            {"role": "user", "content": prompt}
+        ]
+        inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+        # Asynchronous generator to yield tokens as they are generated
+        async def stream_tokens():
+            # Generate tokens one by one
+            for output in model.generate(
+                inputs,
+                max_new_tokens=512,
+                temperature=0.7,
+                top_p=0.9,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id,
+                return_dict_in_generate=True,
+                output_scores=False,
+                streaming=True  # Enable streaming in model.generate (if supported)
+            ):
+                # Decode the latest token
+                token_id = output.sequences[0][-1]  # Get the last generated token
+                token_text = tokenizer.decode([token_id], skip_special_tokens=True)
+                if token_text:
+                    yield token_text
+                    await asyncio.sleep(0.01)  # Small delay to control streaming speed
+                else:
+                    # Handle special tokens or empty outputs
+                    continue
+
+        return stream_tokens()
+
+    except Exception as e:
+        yield f"Error occurred: {e}"
+
+@app.post("/ask")
+async def ask(question: Question):
+    return StreamingResponse(
+        generate_response_chunks(question.question),
+        media_type="text/plain"
+    )