Create app.py

app.py

@@ -0,0 +1,55 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+app = FastAPI()
+
+MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+)
+
+class RequestData(BaseModel):
+    inputs: str
+
+
+def generate_text(prompt):
+
+    # ✅ Proper chat formatting (THIS IS THE FIX)
+    formatted_prompt = f"<|user|>\n{prompt}\n<|assistant|>\n"
+
+    inputs = tokenizer(formatted_prompt, return_tensors="pt")
+
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    result = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    # ✅ Extract only assistant response
+    if "<|assistant|>" in result:
+        result = result.split("<|assistant|>")[-1]
+
+    return result.strip()
+
+
+@app.post("/generate")
+async def generate(request: RequestData):
+
+    text = generate_text(request.inputs)
+
+    return {
+        "data": [text]
+    }