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import gradio as gr
import pickle
import numpy as np
# Load trained model and encoders
model = pickle.load(open("model.pkl", "rb"))
encoders = pickle.load(open("encoders.pkl", "rb"))
def recommend_stack(project_type, team_size, perf_need, experience):
pt = encoders["project_type"].transform([project_type])[0]
pn = encoders["perf_need"].transform([perf_need])[0]
ex = encoders["experience"].transform([experience])[0]
input_data = np.array([[pt, team_size, pn, ex]])
pred_encoded = model.predict(input_data)[0]
return f"🔧 Recommended Tech Stack: {encoders['stack'].inverse_transform([pred_encoded])[0]}"
demo = gr.Interface(
fn=recommend_stack,
inputs=[
gr.Radio(["Web App", "API", "ML App", "Real-time App"], label="Project Type"),
gr.Slider(1, 10, step=1, label="Team Size"),
gr.Radio(["Low", "Medium", "High"], label="Performance Need"),
gr.Radio(["Beginner", "Intermediate", "Expert"], label="Experience Level")
],
outputs="text",
title="Tech Stack Advisor",
description="Get a recommended tech stack based on your project and team!"
)
demo.launch(server_name="0.0.0.0", server_port=5550)
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