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