Spaces:
Running
Running
File size: 2,312 Bytes
0537a74 11511b6 61ea1a8 0537a74 bbc225c 61ea1a8 0537a74 552281c 0537a74 11511b6 777550c 506b0cf 11511b6 777550c df4c303 777550c 3d6604f bbc225c 1a23a7e b760cb4 1a23a7e 0537a74 11511b6 3d6604f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import os
from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import re
from groq import Groq
# Initialize FastAPI app
app = FastAPI()
# Serve static files for assets
app.mount("/static", StaticFiles(directory="static"), name="static")
# Initialize Hugging Face Inference Client
clientHFInference = InferenceClient()
client = Groq()
# Pydantic model for API input
class InfographicRequest(BaseModel):
description: str
# Load prompt template from environment variable
SYSTEM_INSTRUCT = os.getenv("SYSTEM_INSTRUCTOR")
PROMPT_TEMPLATE = os.getenv("PROMPT_TEMPLATE")
async def extract_code_blocks(markdown_text):
"""
Extracts code blocks from the given Markdown text.
Args:
markdown_text (str): The Markdown content as a string.
Returns:
list: A list of code blocks extracted from the Markdown.
"""
# Regex to match code blocks (fenced with triple backticks)
code_block_pattern = re.compile(r'```.*?\n(.*?)```', re.DOTALL)
# Find all code blocks
code_blocks = code_block_pattern.findall(markdown_text)
return code_blocks
# Route to serve the HTML template
@app.get("/", response_class=HTMLResponse)
async def serve_frontend():
return HTMLResponse(open("static/infographic_gen.html").read())
# Route to handle infographic generation
@app.post("/generate")
async def generate_infographic(request: InfographicRequest):
description = request.description
prompt = PROMPT_TEMPLATE.format(description=description)
messages = [{"role": "user", "content": prompt}]
stream = clientHFInference.chat.completions.create(
model="Qwen/Qwen2.5-Coder-32B-Instruct",
messages=messages,
temperature=0.5,
max_tokens=5000,
top_p=0.7,
stream=True,
)
generated_text = ""
for chunk in stream:
generated_text += chunk.choices[0].delta.content
print(generated_text)
code_blocks= await extract_code_blocks(generated_text)
if code_blocks:
return JSONResponse(content={"html": code_blocks[0]})
else:
return JSONResponse(content={"error": "No generation"},status_code=500)
|