Create app.py

app.py

@@ -0,0 +1,44 @@
+from fastapi import FastAPI
+import torch 
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 
+
+app = FastAPI()
+
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!"}
+
+@app.get("/hello/{msg}")
+async def say_hello(msg: str):
+    torch.random.manual_seed(0) 
+    model = AutoModelForCausalLM.from_pretrained( 
+        "microsoft/Phi-3-mini-128k-instruct",  
+        device_map="cpu",  
+        torch_dtype="auto",  
+        trust_remote_code=True,  
+    ) 
+
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct") 
+
+    messages = [ 
+        {"role": "system", "content": "You are a helpful AI assistant."}, 
+        {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, 
+        {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, 
+        {"role": "user", "content": msg}, 
+    ] 
+
+    pipe = pipeline( 
+        "text-generation", 
+        model=model, 
+        tokenizer=tokenizer, 
+    ) 
+
+    generation_args = { 
+        "max_new_tokens": 500, 
+        "return_full_text": False, 
+        "temperature": 0.0, 
+        "do_sample": False, 
+    } 
+
+    output = pipe(messages, **generation_args) 
+    return {"message": output[0]['generated_text']}