Update app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import time
+
+# Mock user database for authentication (in production, this should be more secure)
+users_db = {"admin": "password"}
+
+# In-memory pipelines and logs
+data_pipelines = {}
+logs = []
+
+def log_event(event):
+    timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
+    logs.append(f"[{timestamp}] {event}")
+
+# User Authentication function
+def authenticate(username, password):
+    if username in users_db and users_db[username] == password:
+        log_event(f"User {username} logged in.")
+        return True, f"Welcome, {username}!"
+    else:
+        return False, "Invalid username or password."
+
+# Data ingestion function
+def data_ingestion(data_file):
+    try:
+        df = pd.read_csv(data_file)  # Handle file uploads better in Hugging Face Spaces
+        data_pipelines["data_ingestion"] = df
+        log_event("Data ingestion completed successfully.")
+        return df.head()
+    except Exception as e:
+        log_event(f"Data ingestion failed: {str(e)}")
+        return f"Error: {str(e)}"
+
+# Data transformation function (basic cleaning)
+def data_transformation(clean_duplicates=True, fill_missing=None):
+    if "data_ingestion" not in data_pipelines:
+        return "No data ingested yet."
+    
+    df = data_pipelines["data_ingestion"].copy()
+
+    if clean_duplicates:
+        df = df.drop_duplicates()
+    if fill_missing is not None:
+        df = df.fillna(fill_missing)
+    
+    data_pipelines["data_transformation"] = df
+    log_event("Data transformation completed successfully.")
+    
+    return df.head()
+
+# Data orchestration: sequential task execution
+def run_pipeline():
+    if "data_ingestion" in data_pipelines and "data_transformation" in data_pipelines:
+        log_event("Data pipeline executed successfully.")
+        return "Pipeline executed successfully."
+    else:
+        log_event("Pipeline execution failed.")
+        return "Pipeline execution failed. Please ensure all steps are completed."
+
+# Data monitoring and logs
+def monitor_pipeline():
+    return logs
+
+# Data visualization (displaying the current state of data)
+def visualize_data():
+    if "data_transformation" in data_pipelines:
+        return data_pipelines["data_transformation"].head()
+    else:
+        return "No transformed data available."
+
+# Gradio interface setup
+def main():
+    # Launch Gradio Interface
+    with gr.Blocks() as demo:
+        # Authentication
+        with gr.Row():
+            username_input = gr.Textbox(label="Username")
+            password_input = gr.Textbox(label="Password", type="password")
+            auth_button = gr.Button("Log In")
+            auth_output = gr.Textbox(label="Login Message")
+            auth_button.click(authenticate, inputs=[username_input, password_input], outputs=auth_output)
+        
+        # Ingestion section
+        with gr.Row():
+            ingest_button = gr.File(label="Upload CSV Data")
+            ingest_submit_button = gr.Button("Submit Data")
+            ingest_output = gr.Dataframe(label="Ingested Data")
+            ingest_submit_button.click(data_ingestion, inputs=ingest_button, outputs=ingest_output)
+
+        # Transformation section
+        with gr.Row():
+            clean_duplicates = gr.Checkbox(label="Remove Duplicates", value=True)
+            fill_missing = gr.Textbox(label="Fill Missing Values (leave empty for no filling)")
+            transform_button = gr.Button("Transform Data")
+            transform_output = gr.Dataframe(label="Transformed Data")
+            transform_button.click(data_transformation, inputs=[clean_duplicates, fill_missing], outputs=transform_output)
+
+        # Orchestration section
+        with gr.Row():
+            pipeline_button = gr.Button("Run Pipeline")
+            pipeline_output = gr.Textbox(label="Pipeline Status")
+            pipeline_button.click(run_pipeline, outputs=pipeline_output)
+
+        # Monitoring section
+        with gr.Row():
+            monitor_button = gr.Button("Monitor Pipeline")
+            monitor_output = gr.Textbox(label="Logs")
+            monitor_button.click(monitor_pipeline, outputs=monitor_output)
+
+        # Visualization section
+        with gr.Row():
+            visualize_button = gr.Button("Visualize Data")
+            visualize_output = gr.Dataframe(label="Data Preview")
+            visualize_button.click(visualize_data, outputs=visualize_output)
+
+    demo.launch()
+
+# Launch the app
+if __name__ == "__main__":
+    main()