Create app.py

app.py

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+import gradio as gr
+from datasets import load_dataset
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+# Load dataset and build vectorizer
+ds = load_dataset("STEM‑AI‑mtl/electrical‑engineering", split="train")
+docs = [q + " " + a for q, a in zip(ds["question"], ds["answer"])]
+vectorizer = TfidfVectorizer().fit(docs)
+tfidf_matrix = vectorizer.transform(docs)
+
+def get_answer(user_question, top_k=1):
+    user_vec = vectorizer.transform([user_question])
+    sims = cosine_similarity(user_vec, tfidf_matrix).flatten()
+    idxs = np.argsort(-sims)[:top_k]
+    return "\n\n".join([f"**Q:** {ds['question'][i]}\n**A:** {ds['answer'][i]}" for i in idxs])
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🛠️ Electronics Q&A Assistant")
+    question = gr.Textbox(label="Ask a question about electronics", lines=2)
+    answer = gr.Markdown()
+    ask_btn = gr.Button("Get Answer")
+    ask_btn.click(fn=get_answer, inputs=question, outputs=answer)
+
+if __name__ == "__main__":
+    demo.launch()