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import os
import json
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import StreamingResponse
from fastapi import APIRouter
from huggingface_hub import AsyncInferenceClient
from .utils import handle_attachments, extract_text_from_pdf
router = APIRouter()
HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
client = AsyncInferenceClient(api_key=HUGGINGFACE_TOKEN)
attachments_in_huggingface = {}
@router.post("/huggingface_stream")
async def huggingface_stream(request: Request):
try:
body = await request.json()
except Exception as e:
raise HTTPException(status_code=400, detail="Invalid JSON payload") from e
conversation = body.get("messages")
if not conversation:
raise HTTPException(status_code=400, detail="Missing 'conversation' in payload")
print("--------------------------------")
print(body)
print()
temperature = body.get("temperature", 0.7)
max_tokens = body.get("max_tokens", 256)
model = body.get("model", "meta-llama/Llama-3.3-70B-Instruct")
# Get session ID from the request
session_id = request.headers.get("X-Session-ID")
if session_id not in attachments_in_huggingface: attachments_in_huggingface[session_id] = {}
if not session_id:
raise HTTPException(status_code=400, detail="Missing 'session_id' in payload")
# Handle file attachments if present)
conversation = await handle_attachments(session_id, conversation)
huggingface_messages = []
for msg in conversation:
role = "user" if msg["role"] == "user" else "assistant"
pdf_texts = []
if "attachments" in msg:
for attachment in msg["attachments"]:
if attachment["file_path"].endswith(".pdf"):
if attachment["file_path"] not in attachments_in_huggingface[session_id]:
pdf_text = await extract_text_from_pdf(attachment["file_path"])
pdf_texts.append([attachment["name"], pdf_text])
attachments_in_huggingface[session_id][attachment["name"]] = pdf_text
else:
pdf_texts.append([attachment["name"], attachments_in_huggingface[session_id][attachment["name"]]])
huggingface_messages.append({"role": role, "content": msg["content"]})
for pdf_text in pdf_texts:
huggingface_messages.append({"role": "user", "content": f"{pdf_text[0]}\n\n{pdf_text[1]}"})
async def event_generator():
try:
print(f"Starting stream for model: {model}, temperature: {temperature}, max_tokens: {max_tokens}")
line_count = 0
# Use the SDK to create a streaming completion
stream = await client.chat.completions.create(
model=model,
messages=huggingface_messages,
temperature=temperature,
max_tokens=max_tokens,
stream=True
)
async for chunk in stream:
if chunk.choices and chunk.choices[0].delta.content is not None:
content = chunk.choices[0].delta.content
line_count += 1
if line_count % 10 == 0:
print(f"Processed {line_count} stream chunks")
# Format the response in the same way as before
response_json = json.dumps({
"choices": [{"delta": {"content": content}}]
})
yield f"data: {response_json}\n\n"
# Send the [DONE] marker
print("Stream completed successfully")
yield "data: [DONE]\n\n"
except Exception as e:
print(f"Error during streaming: {str(e)}")
yield f"data: {{\"error\": \"{str(e)}\"}}\n\n"
finally:
print(f"Stream ended after processing {line_count if 'line_count' in locals() else 0} chunks")
print("Returning StreamingResponse to client")
return StreamingResponse(event_generator(), media_type="text/event-stream") |