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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from huggingface_hub import InferenceClient
app = FastAPI()
# Use your model
client = InferenceClient("ManojINaik/codsw")
class Item(BaseModel):
prompt: str
history: list
system_prompt: str
temperature: float = 0.0
max_new_tokens: int = 1048
top_p: float = 0.15
repetition_penalty: float = 1.0
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def generate(item: Item):
try:
# Ensure valid temperature
temperature = max(float(item.temperature), 1e-2)
top_p = float(item.top_p)
generate_kwargs = {
"temperature": temperature,
"max_new_tokens": item.max_new_tokens,
"top_p": top_p,
"repetition_penalty": item.repetition_penalty,
"do_sample": True,
"seed": 42,
}
# Format the prompt
formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
# Call text_generation on your model (correct argument: formatted_prompt)
stream = client.text_generation(
formatted_prompt, # Use the formatted prompt directly
**generate_kwargs,
stream=True,
)
output = "".join([response.token.text for response in stream])
return output
except Exception as e:
raise HTTPException(status_code=500, detail=f"Internal error: {str(e)}")
@app.post("/generate/")
async def generate_text(item: Item):
return {"response": generate(item)}
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