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| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| # Load NLP Model (example with Hugging Face pipeline) | |
| def initialize_model(): | |
| return pipeline("text-davinci-003") # Replace with your fine-tuned model if needed | |
| # Circuit Design Logic (Stub for now) | |
| def circuit_design(query): | |
| if "amplifier" in query.lower(): | |
| return { | |
| "design": "Use an operational amplifier with a gain of 10x. Suggested op-amp: LM741.", | |
| "schematic": "Example circuit diagram: Input -> Resistor (10k) -> Op-Amp -> Output." | |
| } | |
| elif "microcontroller" in query.lower(): | |
| return { | |
| "design": "Use a PIC16F877A for basic embedded applications.", | |
| "schematic": "Connect power pins, I/O peripherals, and crystal oscillator (20 MHz)." | |
| } | |
| else: | |
| return {"design": "Design not found.", "schematic": ""} | |
| # Testing Guidance Logic (Stub for now) | |
| def testing_guidance(query): | |
| if "amplifier" in query.lower(): | |
| return "Test using an oscilloscope to measure input and output signals. Ensure expected gain." | |
| elif "microcontroller" in query.lower(): | |
| return "Test by flashing firmware and checking functionality of I/O peripherals." | |
| else: | |
| return "No specific testing guidance found." | |
| # Chatbot response generation | |
| def chatbot_response(query): | |
| nlp_model = initialize_model() | |
| general_response = nlp_model(query) | |
| # Enhance response for circuit-specific queries | |
| if any(keyword in query.lower() for keyword in ["design", "circuit", "schematic"]): | |
| circuit_info = circuit_design(query) | |
| return general_response["generated_text"] + f"\n\nDesign Guidance: {circuit_info['design']}\nSchematic: {circuit_info['schematic']}" | |
| elif "test" in query.lower(): | |
| return general_response["generated_text"] + f"\n\nTesting Guidance: {testing_guidance(query)}" | |
| return general_response["generated_text"] | |
| # Gradio Interface | |
| def interface(query): | |
| response = chatbot_response(query) | |
| return response | |
| # Launch Gradio App | |
| with gr.Blocks() as app: | |
| gr.Markdown("# Embedded Systems Chatbot") | |
| with gr.Row(): | |
| user_query = gr.Textbox(label="Enter your query:", placeholder="Ask about circuit design, components, or testing...") | |
| response = gr.Textbox(label="Response:", interactive=False) | |
| submit_button = gr.Button("Submit") | |
| submit_button.click(interface, inputs=[user_query], outputs=[response]) | |
| app.launch() | |