App.py running locally (w/ gemma3:12b from Ollama)

app.py

@@ -8,10 +8,10 @@ import re
 import pandas as pd  # type: ignore
 from dotenv import load_dotenv  # type: ignore # Para cambios locales
 from supabase import create_client, Client  # type: ignore
-from transformers import pipeline
+# from transformers import pipeline
 
 from pandasai import SmartDataframe  # type: ignore
-from pandasai.llm.starcoder import Starcoder # type: ignore
+from pandasai.llm.local_llm import LocalLLM
 
 # ---------------------------------------------------------------------------------
 # Funciones auxiliares
@@ -94,6 +94,36 @@ data = load_data("labor")
 # population_data = load_data("population")
 # predictions_data = load_data("predictions")
 
+"""
+# Ej:
+# import os
+# import pandas as pd
+# from pandasai import SmartDatalake
+
+# employees_data = {
+#     'EmployeeID': [1, 2, 3, 4, 5],
+#     'Name': ['John', 'Emma', 'Liam', 'Olivia', 'William'],
+#     'Department': ['HR', 'Sales', 'IT', 'Marketing', 'Finance']
+# }
+
+# salaries_data = {
+#     'EmployeeID': [1, 2, 3, 4, 5],
+#     'Salary': [5000, 6000, 4500, 7000, 5500]
+# }
+
+# employees_df = pd.DataFrame(employees_data)
+# salaries_df = pd.DataFrame(salaries_data)
+
+# # By default, unless you choose a different LLM, it will use BambooLLM.
+# # You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)
+# os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
+
+# lake = SmartDatalake([employees_df, salaries_df])
+# lake.chat("Who gets paid the most?")
+# # Output: Olivia gets paid the most
+
+"""
+
 # ---------------------------------------------------------------------------------
 # Inicializar modelo LLM
 # ---------------------------------------------------------------------------------
@@ -106,11 +136,16 @@ data = load_data("labor")
 # Inicializar PandasAI con StarCoder
 # ---------------------------------------------------------------------------------
 
-# Definir el modelo StarCoder desde Hugging Face
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-llm = Starcoder(api_token=huggingface_token)
+# # Definir el modelo StarCoder desde Hugging Face
+# huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+# llm = Starcoder(api_token=huggingface_token)
+
+ollama_llm = LocalLLM(api_base="http://localhost:11434/v1", 
+                      model="gemma3:12b",
+                      temperature=0.1,  
+                      max_tokens=8000)
 
-sdf = SmartDataframe(data, config={"llm": llm}) # DataFrame PandasAI-ready.
+sdf = SmartDataframe(data, config={"llm": ollama_llm}) # DataFrame PandasAI-ready.
 
 # ---------------------------------------------------------------------------------
 # Configuración de la app en Streamlit
@@ -123,28 +158,16 @@ st.title("_Europe GraphGen_  :blue[Graph generator] :flag-eu:")
 user_input = st.text_input("What graphics do you have in mind")
 generate_button = st.button("Generate")
 
-# Manejo de evento de botón
+# Procesar el input del usuario con PandasAI
 if generate_button and user_input:
-    # if data.empty and supabase is not None:
-    #     st.warning("Successfully connected to Supabase, but no data was loaded (either the table is empty or there was a query issue). Check the error message above if any.")
-    # elif not data.empty:
-    #     st.success("Successfully connected to Supabase and loaded data!")
-    #     st.dataframe(data.head())  # Mostrar una pequeña muestra del DataFrame
-    # elif supabase is None:
-    #     st.error("Failed to initialize Supabase client. Check environment variables in Settings.")
-    # else:
-    #     st.info("Attempted to load data. Check for any error messages above.")
-
-    # Procesar el input del usuario con PandasAI
-    if generate_button and user_input:
-        st.dataframe(data.head())
-        
-        with st.spinner('Generating answer...'):
-            try:
-                answer = sdf.chat(user_input)
-                st.write(answer)
-            except Exception as e:
-                st.error(f"Error generating answer: {e}")
+    st.dataframe(data.head())
+    
+    with st.spinner('Generating answer...'):
+        try:
+            answer = sdf.chat(user_input)
+            st.write(answer)
+        except Exception as e:
+            st.error(f"Error generating answer: {e}")
 
 
 # TODO: Output estructurado si vemos que es necesario.