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import gradio as gr
import requests
from bs4 import BeautifulSoup
from transformers import pipeline
task_extractor = pipeline("text2text-generation", model="google/flan-t5-small")
# Simulated LLM task extraction (replace with real call if local)
def extract_task(user_input):
prompt = f"Classify the following ML task: {user_input}. Just reply with the task name."
result = task_extractor(prompt, max_new_tokens=10)
task = result[0]["generated_text"].strip().lower()
return task
# Scrape Hugging Face models by task
def get_models_for_task(task):
url = f"https://huggingface.co/models?pipeline_tag={task}"
headers = {"User-Agent": "Mozilla/5.0"}
try:
res = requests.get(url, headers=headers)
soup = BeautifulSoup(res.text, "html.parser")
results = []
for a in soup.find_all("a", class_="flex items-center gap-2"):
model_name = a.get("href", "").strip("/").split("/")[-1]
if model_name:
results.append(model_name)
if len(results) >= 5:
break
return results if results else ["No models found"]
except Exception as e:
return [f"Error: {str(e)}"]
# Combined predict function
def predict(user_input):
task = extract_task(user_input)
models = get_models_for_task(task)
return f"🧠 Task: {task}\n📦 Models:\n" + "\n".join(models)
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## 🤖 ML Task → Hugging Face Model Finder")
with gr.Row():
input_box = gr.Textbox(label="Describe your ML task")
submit_btn = gr.Button("🔍 Find Models")
output_box = gr.Textbox(label="Suggested Models", lines=10)
submit_btn.click(predict, inputs=input_box, outputs=output_box)
demo.launch()
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