Update app.py

app.py

@@ -1,6 +1,26 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+import csv
+import json
+
+def buscar_en_csv_y_generar_json(archivo_csv, valor_busqueda):
+    resultados = []
+
+    with open(archivo_csv, mode='r', encoding='utf-8') as file:
+        reader = csv.reader(file)
+        
+        for fila in reader:
+            linea_completa = ','.join(fila)
+            if valor_busqueda in linea_completa:
+                resultados.append(fila)
+
+    if resultados:
+        return json.dumps(resultados, indent=4, ensure_ascii=False)
+    else:
+        return json.dumps({"mensaje": "No se encontraron coincidencias."}, indent=4, ensure_ascii=False)
+
+
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
@@ -43,7 +63,10 @@ def respond(
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
 css = "#component-2 {height: 350px}"
+def search(term):
 
+    return buscar_en_csv_y_generar_json("proyectos_empresas.csv", term)
+    
 with gr.Blocks(title="Sophia, Torah Codes",css=css) as app:
 #with gr.Blocks(theme='gradio/soft') as demo:
 #with gr.Blocks(title="Sophia, Torah Codes") as app:
@@ -67,11 +90,11 @@ with gr.Blocks(title="Sophia, Torah Codes",css=css) as app:
         search_els = gr.Button("Search",scale=1)
     with gr.Row():
         #els_results = gr.JSON(label="Results")
-        els_results = gr.Dataframe(type="pandas")
+        results = gr.JSON()
         search_els.click(
-            els_book, 
+            search, 
             inputs=[to_convert,to_convert],
-            outputs=els_results
+            outputs= results
         )