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README.md

@@ -1,12 +1,21 @@
 ---
-title: Data Analytics WithAI
-emoji: 📉
-colorFrom: yellow
-colorTo: gray
+title: AI CSV Data Analyst
+emoji: 📊
+colorFrom: indigo
+colorTo: blue
 sdk: streamlit
-sdk_version: 1.42.0
+sdk_version: "1.35.0"
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 📊 AI CSV Data Analyst
+
+Esta aplicación permite subir archivos `.csv` o `.xlsx` y hacer análisis estadísticos, visualizaciones y preguntas impulsadas por la API de Gemini (Google Generative AI).
+
+## 🚀 Características
+
+- Visualización automática de datos
+- Estadísticas solo si hay columnas numéricas
+- Interacción con IA usando Gemini
+- Compatible con Hugging Face Spaces

app.py

@@ -1,89 +1,102 @@
-import streamlit as st
-import pandas as pd
-import google.generativeai as genai
-import matplotlib.pyplot as plt
-import seaborn as sns
-import plotly.express as px
-
-# Set up page layout
-st.set_page_config(page_title="AI CSV Data Analyst", layout="wide")
-
-# Initialize Gemini API (Replace with your API Key)
-GEMINI_API_KEY = "AIzaSyDt0TM6beHrE-f5bvfYXQa6iDACSCfU7go"
-genai.configure(api_key=GEMINI_API_KEY)
-
-# Create two columns (70:30 split)
-left_col, right_col = st.columns([7, 3])
-
-with left_col:
-    st.title("📊 AI-Powered CSV Data Analyst")
-
-    # File Upload
-    uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])
-
-    if uploaded_file is not None:
-        # Read File
-        file_ext = uploaded_file.name.split(".")[-1]
-        if file_ext == "csv":
-            df = pd.read_csv(uploaded_file)
-        elif file_ext == "xlsx":
-            df = pd.read_excel(uploaded_file, engine="openpyxl")
-
-        # Display the DataFrame
-        st.subheader("📂 Uploaded Data")
-        st.dataframe(df)
-
-        # Data Insights
-        st.subheader("📈 Data Insights")
-        
-        # Dataset Summary
-        st.write(f"**Rows:** {df.shape[0]}, **Columns:** {df.shape[1]}")
-        st.write(f"**Missing Values:**\n{df.isnull().sum()}")
-
-        # Basic Statistics
-        st.subheader("📊 Statistical Summary")
-        st.write(df.describe())
-
-        # Visualizations
-        st.subheader("📉 Data Visualizations")
-
-        # Select Column for Histogram
-        numeric_columns = df.select_dtypes(include=["number"]).columns
-        if len(numeric_columns) > 0:
-            col = st.selectbox("Select a column for histogram:", numeric_columns)
-            fig = px.histogram(df, x=col, title=f"Histogram of {col}")
-            st.plotly_chart(fig)
-
-        # Correlation Heatmap
-        if len(numeric_columns) > 1:
-            st.subheader("🔍 Correlation Heatmap")
-            fig, ax = plt.subplots(figsize=(6, 4))
-            sns.heatmap(df[numeric_columns].corr(), annot=True, cmap="coolwarm", ax=ax)
-            st.pyplot(fig)
-
-with right_col:
-    st.subheader("💬 Chat with Your Data")
-
-    user_query = st.text_input("Ask a question about the data...")
-
-    if user_query and uploaded_file is not None:
-        # Prepare prompt for AI
-        prompt = f"""
-        You are a data analyst. The user has uploaded a dataset. 
-        Answer the query based on the dataset provided.
-
-        Dataset Overview:
-        {df.describe().to_string()}
-
-        User Question:
-        {user_query}
-        """
-
-        # Get AI response
-        model = genai.GenerativeModel("gemini-pro")
-        response = model.generate_content(prompt)
-
-        # Display AI Response
-        if response.text:
-            st.write("🤖 AI Response:")
-            st.write(response.text)
+import streamlit as st
+import pandas as pd
+import google.generativeai as genai
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+
+# Set up page layout
+st.set_page_config(page_title="AI CSV Data Analyst", layout="wide")
+
+# Initialize Gemini API (Replace with your API Key)
+import os
+GEMINI_API_KEY = "AIzaSyDt0TM6beHrE-f5bvfYXQa6iDACSCfU7go"
+genai.configure(api_key=GEMINI_API_KEY)
+
+# Create two columns (70:30 split)
+left_col, right_col = st.columns([7, 3])
+
+with left_col:
+    st.title("📊 AI-Powered CSV Data Analyst")
+
+    # File Upload
+    uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])
+
+    if uploaded_file is not None:
+        # Read File
+        file_ext = uploaded_file.name.split(".")[-1]
+        if file_ext == "csv":
+            df = pd.read_csv(uploaded_file, dtype=str)
+        elif file_ext == "xlsx":
+            df = pd.read_excel(uploaded_file, engine="openpyxl", dtype=str)
+
+        import numpy as np
+# Display the DataFrame
+        st.subheader("📂 Uploaded Data")
+        st.dataframe(df)
+
+        # Data Insights
+        st.subheader("📈 Data Insights")
+
+        # Dataset Summary
+        st.write(f"**Rows:** {df.shape[0]}, **Columns:** {df.shape[1]}")
+        st.write(f"**Missing Values:**")
+        st.write(df.isnull().sum())
+
+        # Basic Statistics
+        st.subheader("📊 Statistical Summary")
+        numeric_df = df.apply(pd.to_numeric, errors='coerce')
+        if numeric_df.select_dtypes(include=['number']).shape[1] > 0:
+            st.write(numeric_df.describe())
+        else:
+            st.info("No hay columnas numéricas para mostrar estadísticas.")
+
+        # Visualizations
+        st.subheader("📉 Data Visualizations")
+
+        # Select Column for Histogram
+        numeric_columns = df.select_dtypes(include=["number"]).columns
+        if len(numeric_columns) > 0:
+            col = st.selectbox("Select a column for histogram:", numeric_columns)
+            fig = px.histogram(df, x=col, title=f"Histogram of {col}")
+            st.plotly_chart(fig)
+
+        # Correlation Heatmap
+        if len(numeric_columns) > 1:
+            st.subheader("🔍 Correlation Heatmap")
+            fig, ax = plt.subplots(figsize=(6, 4))
+            sns.heatmap(df[numeric_columns].corr(), annot=True, cmap="coolwarm", ax=ax)
+            st.pyplot(fig)
+
+with right_col:
+    st.subheader("💬 Chat with Your Data")
+
+    user_query = st.text_input("Ask a question about the data...")
+
+    if user_query and uploaded_file is not None:
+        # Prepare prompt for AI (limited to 5 columns and rounded)
+        numeric_df = df.apply(pd.to_numeric, errors="coerce")
+        safe_summary = numeric_df.describe().round(2).iloc[:, :5]
+
+        prompt = f"""
+        You are a data analyst. The user has uploaded a dataset. 
+        Answer the query based on the dataset provided.
+
+        Dataset Overview (first 5 numeric columns):
+        {safe_summary.to_string()}
+
+        User Question:
+        {user_query}
+        """
+
+        try:
+            model = genai.GenerativeModel("models/gemini-pro")
+            response = model.generate_content(prompt)
+
+            if hasattr(response, "text"):
+                st.write("🤖 AI Response:")
+                st.write(response.text)
+            else:
+                st.error("No se recibió una respuesta válida del modelo.")
+        except Exception as e:
+            st.error(f"Error al consultar el modelo Gemini: {e}")

requirements.txt

@@ -1,6 +1,7 @@
-google-generativeai
-seaborn
+streamlit
+pandas
 matplotlib
+seaborn
 plotly
-openpyxl
-pandas
+google-generativeai
+openpyxl