Create app.py

app.py

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+import gradio as gr
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import pandas as pd
+
+# Load dataset
+dataset = load_dataset("Levimichael4/BioHackBuddy-Healthadvice", split="train")
+df = pd.DataFrame(dataset)
+
+# Load embedding model
+model = SentenceTransformer("all-MiniLM-L6-v2")
+issue_embeddings = model.encode(df["Issue"].tolist(), convert_to_tensor=True)
+
+# Recommend top 3 similar entries
+def recommend(user_input):
+    input_emb = model.encode([user_input], convert_to_tensor=True)
+    sims = cosine_similarity(input_emb, issue_embeddings)[0]
+    top_indices = sims.argsort()[-3:][::-1]
+    results = df.iloc[top_indices][["Issue", "Suggestion 1", "Suggestion 2", "Suggestion 3"]]
+    return results.to_markdown(index=False)
+
+# Gradio UI
+demo = gr.Interface(
+    fn=recommend,
+    inputs=gr.Textbox(label="Describe your issue or health goal"),
+    outputs=gr.Markdown(label="Top 3 Suggestions"),
+    examples=[
+        ["I feel tired every morning"],
+        ["I want to improve focus"],
+        ["I can't sleep well at night"]
+    ],
+    title="🧠 BioHackBuddy - Personalized Wellness Advice",
+    description="Get science-backed lifestyle suggestions based on your personal wellness challenge or goal."
+)
+
+demo.launch()
+