Update app.py

app.py

@@ -7,27 +7,27 @@ from transformers import (
 )
 from langchain_huggingface import HuggingFacePipeline
 from langchain_core.prompts import PromptTemplate
-from langchain.chains import LLMChain
+from langchain_core.runnables import RunnableSequence
 
 # — Model setup —
 MODEL_ID = "bigcode/starcoder2-3b"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+# Explicitly set pad_token_id to eos_token_id
+tokenizer.pad_token_id = tokenizer.eos_token_id
 model = AutoModelForCausalLM.from_pretrained(MODEL_ID, trust_remote_code=True)
 
-# — Pipeline setup (pass generation parameters directly) —
+# — Pipeline setup (remove unused parameters, set device explicitly) —
 pipe = pipeline(
     "text-generation",
     model=model,
     tokenizer=tokenizer,
-    device_map="auto",
+    device=-1,  # Explicitly use CPU; change to 0 or "cuda" if GPU available
     max_new_tokens=64,
-    temperature=0.2,
-    top_p=0.95,
     do_sample=False,
 )
 llm = HuggingFacePipeline(pipeline=pipe)
 
-# — Prompt & chain —
+# — Prompt & chain (using RunnableSequence) —
 prompt = PromptTemplate(
     input_variables=["description"],
     template=(
@@ -36,7 +36,7 @@ prompt = PromptTemplate(
         "Emmet:"
     ),
 )
-chain = LLMChain(llm=llm, prompt=prompt)
+chain = RunnableSequence(prompt | llm)
 
 # — FastAPI app —
 app = FastAPI()
@@ -47,8 +47,12 @@ class Req(BaseModel):
 class Res(BaseModel):
     emmet: str
 
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Emmet Generator API. Use POST /generate-emmet."}
+
 @app.post("/generate-emmet", response_model=Res)
 async def generate_emmet(req: Req):
-    raw = chain.invoke(req.description)  # use .invoke() instead of deprecated .run()
+    raw = chain.invoke(req.description)
     emmet = raw.strip().splitlines()[0]
     return {"emmet": emmet}