Update app.py

app.py

@@ -54,30 +54,45 @@ agent = CodeAgent(
 )
 
 def run_agent(_):
-    global df_global
     if df_global is None:
-        return "No data available. Please upload a file first."
-
-    temp_path = "./temp_cleaned_data.csv"
-    df_global.to_csv(temp_path, index=False)
-
-    analysis_result = agent.run(
-        """
-        You are an expert data analyst. Perform comprehensive analysis including:
-        1. Basic statistics and data quality checks.
-        2. Three insightful analytical questions about relationships in the data.
-        3. Visualization of key patterns and correlations.
-        4. Actionable real-world insights derived from findings.
-        Generate publication-quality visualizations and save them to './figures/'.
-        """,
-        additional_args={
-            "additional_notes": additional_notes,
-            "source_file": temp_path
-        }
+        return "Please upload a file first."
+
+    from tempfile import NamedTemporaryFile
+    temp_file = NamedTemporaryFile(delete=False, suffix=".csv")
+    df_global.to_csv(temp_file.name, index=False)
+    temp_file.close()
+
+    prompt = """
+    You are an expert data analyst.
+    1. Load the provided dataset and analyze the structure.
+    2. Automatically detect key numeric and categorical columns.
+    3. Perform:
+       - Basic descriptive statistics.
+       - Null and duplicate checks.
+       - Insightful relationships between key columns.
+       - At least 3 visualizations showing important trends.
+    4. Derive at least 3 actionable real-world insights.
+    5. Save all visualizations to ./figures/ directory.
+    Return:
+      - A summary of the insights in clean bullet-point format.
+      - File paths of the generated visualizations.
+    """
+
+    result = agent.run(
+        prompt,
+        additional_args={"source_file": temp_file.name}
     )
 
-   # print(analysis_result)  # Safe to print here if you're debugging
-    return analysis_result
+    # Extract image paths from output (assuming SmolAgent prints them)
+    image_paths = []
+    for line in result.splitlines():
+        if line.strip().endswith(".png"):
+            image_paths.append(line.strip())
+
+    insights = "\n".join([line for line in result.splitlines() if not line.strip().endswith(".png")])
+
+    return insights, image_paths
+