app.py

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()