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app.py
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import os
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from fastapi import FastAPI, Request
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel
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from huggingface_hub import InferenceClient
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import gradio as gr
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import re
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# Initialize FastAPI app
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app = FastAPI()
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# Initialize Hugging Face Inference Client
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clientHFInference = InferenceClient()
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# Pydantic model for API input
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class InfographicRequest(BaseModel):
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description: str
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# Load prompt template from environment variable
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SYSTEM_INSTRUCT = os.getenv("SYSTEM_INSTRUCTOR", "Generate a high-quality infographic based on the description provided.")
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PROMPT_TEMPLATE = os.getenv("PROMPT_TEMPLATE", "Create an infographic with the following details: {description}")
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async def extract_code_blocks(markdown_text):
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"""
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Extracts code blocks from the given Markdown text.
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"""
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code_block_pattern = re.compile(r'```.*?\n(.*?)```', re.DOTALL)
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code_blocks = code_block_pattern.findall(markdown_text)
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return code_blocks
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@app.post("/generate")
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async def generate_infographic(request: InfographicRequest):
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description = request.description
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prompt = PROMPT_TEMPLATE.format(description=description)
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response = clientHFInference.text_to_image(
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model="stabilityai/stable-diffusion-xl", # Using an advanced image-based AI model
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inputs=prompt
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)
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generated_image_url = response.get("image_url", None)
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if generated_image_url:
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return JSONResponse(content={"image_url": generated_image_url})
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else:
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return JSONResponse(content={"error": "No infographic generated"}, status_code=500)
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# Gradio UI for Hugging Face Spaces
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def generate_infographic_ui(description):
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response = generate_infographic(InfographicRequest(description=description))
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return response["image_url"] if "image_url" in response else "Error generating infographic"
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demo = gr.Interface(
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fn=generate_infographic_ui,
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inputs="text",
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outputs="image",
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title="AI Infographic Generator",
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description="Enter a description, and the AI will generate a high-quality infographic."
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)
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demo.launch(share=True)
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