Update app.py

app.py

@@ -7,8 +7,6 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import os
 from groq import Groq
-from dataclasses import dataclass
-import tempfile
 import base64
 import io
 
@@ -39,7 +37,11 @@ class GroqLLM:
                 stream=False
             )
             
-            return completion.choices[0].message.content if completion.choices else "Error: No response generated"
+            # Ensure the response is properly formatted
+            if completion.choices and hasattr(completion.choices[0].message, 'content'):
+                return completion.choices[0].message.content
+            else:
+                return "Error: No valid response generated from the model."
             
         except Exception as e:
             error_msg = f"Error generating response: {str(e)}"
@@ -76,21 +78,7 @@ class DataAnalysisAgent(CodeAgent):
 
 @tool
 def analyze_basic_stats(data: pd.DataFrame) -> str:
-    """Calculate basic statistical measures for numerical columns in the dataset.
-    
-    This function computes fundamental statistical metrics including mean, median,
-    standard deviation, skewness, and counts of missing values for all numerical
-    columns in the provided DataFrame.
-    
-    Args:
-        data: A pandas DataFrame containing the dataset to analyze. The DataFrame
-            should contain at least one numerical column for meaningful analysis.
-    
-    Returns:
-        str: A string containing formatted basic statistics for each numerical column,
-            including mean, median, standard deviation, skewness, and missing value counts.
-    """
-    # Access dataset from agent if no data provided
+    """Calculate basic statistical measures for numerical columns in the dataset."""
     if data is None:
         data = tool.agent.dataset
     
@@ -110,21 +98,7 @@ def analyze_basic_stats(data: pd.DataFrame) -> str:
 
 @tool
 def generate_correlation_matrix(data: pd.DataFrame) -> str:
-    """Generate a visual correlation matrix for numerical columns in the dataset.
-    
-    This function creates a heatmap visualization showing the correlations between
-    all numerical columns in the dataset. The correlation values are displayed
-    using a color-coded matrix for easy interpretation.
-    
-    Args:
-        data: A pandas DataFrame containing the dataset to analyze. The DataFrame
-            should contain at least two numerical columns for correlation analysis.
-    
-    Returns:
-        str: A base64 encoded string representing the correlation matrix plot image,
-            which can be displayed in a web interface or saved as an image file.
-    """
-    # Access dataset from agent if no data provided
+    """Generate a visual correlation matrix for numerical columns in the dataset."""
     if data is None:
         data = tool.agent.dataset
         
@@ -141,20 +115,7 @@ def generate_correlation_matrix(data: pd.DataFrame) -> str:
 
 @tool
 def analyze_categorical_columns(data: pd.DataFrame) -> str:
-    """Analyze categorical columns in the dataset for distribution and frequencies.
-    
-    This function examines categorical columns to identify unique values, top categories,
-    and missing value counts, providing insights into the categorical data distribution.
-    
-    Args:
-        data: A pandas DataFrame containing the dataset to analyze. The DataFrame
-            should contain at least one categorical column for meaningful analysis.
-    
-    Returns:
-        str: A string containing formatted analysis results for each categorical column,
-            including unique value counts, top categories, and missing value counts.
-    """
-    # Access dataset from agent if no data provided
+    """Analyze categorical columns in the dataset for distribution and frequencies."""
     if data is None:
         data = tool.agent.dataset
         
@@ -172,20 +133,7 @@ def analyze_categorical_columns(data: pd.DataFrame) -> str:
 
 @tool
 def suggest_features(data: pd.DataFrame) -> str:
-    """Suggest potential feature engineering steps based on data characteristics.
-    
-    This function analyzes the dataset's structure and statistical properties to
-    recommend possible feature engineering steps that could improve model performance.
-    
-    Args:
-        data: A pandas DataFrame containing the dataset to analyze. The DataFrame
-            can contain both numerical and categorical columns.
-    
-    Returns:
-        str: A string containing suggestions for feature engineering based on
-            the characteristics of the input data.
-    """
-    # Access dataset from agent if no data provided
+    """Suggest potential feature engineering steps based on data characteristics."""
     if data is None:
         data = tool.agent.dataset
         
@@ -215,7 +163,8 @@ def main():
     if 'agent' not in st.session_state:
         st.session_state['agent'] = None
     
-    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    # Drag-and-drop file upload
+    uploaded_file = st.file_uploader("Drag and drop a CSV file here", type="csv")
     
     try:
         if uploaded_file is not None: