Create app.py

app.py

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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()