try query

app.py

@@ -2,20 +2,35 @@ import gradio as gr
 import chromadb
 import pandas as pd
 
-def greet(name):
-    client = chromadb.Client()
-    collection = client.create_collection("bolivian-recipes")
-    df = pd.read_parquet("hf://datasets/asoria/bolivian-recipes@~parquet/default/other/0000.parquet")
-    text_column = "preparation"
-    ids = [str(i) for i in range(df.shape[0])]
-    documents = df[text_column].to_list()
-    metadatas = df.drop(text_column, axis=1).to_dict("records")
+client = chromadb.Client()
+collection = client.create_collection("bolivian-recipes")
+df = pd.read_parquet("hf://datasets/asoria/bolivian-recipes@~parquet/default/other/0000.parquet")
+text_column = "preparation"
+ids = [str(i) for i in range(df.shape[0])]
+documents = df[text_column].to_list()
+metadatas = df.drop(text_column, axis=1).to_dict("records")
+collection.add(ids=ids, documents=documents, metadatas=metadatas)
 
-    collection.add(ids=ids, documents=documents, metadatas=metadatas)
 
-    results = collection.query(query_texts=["sausage"], n_results=4)
-    print(results)
-    return "Hello " + name + "!!"
+with gr.Blocks() as demo:
+    gr.Markdown(" ## PandasAI demo using datasets library")
+    gr.Markdown(" pandasai library https://github.com/gventuri/pandas-ai")
+    gr.Markdown(" datasets library https://huggingface.co/docs/datasets")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    dataset = gr.Textbox(label="dataset", placeholder="mstz/iris", value="mstz/iris")
+    config = gr.Textbox(label="config", placeholder="iris", value="iris")
+    split = gr.Textbox(label="split", placeholder="train", value="train")
+    prompt = gr.Textbox(label="prompt (str)", placeholder="How many records do I have?. Give me the median of sepal_width. Show me the first 5 rows.")
+    cached_responses_table = gr.DataFrame()
+    get_result_button = gr.Button("Submit")
+
+    def get_result(dataset, config, split, prompt) -> str:
+        result = collection.query(query_texts=[prompt], n_results=4)
+        return {
+                    cached_responses_table: gr.update(value=result)
+        }
+
+    get_result_button.click(get_result, inputs=[dataset, config, split, prompt], outputs=cached_responses_table)
+
+if __name__ == "__main__":
+    demo.launch(debug=True)