Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import random
+
+def analyze_data(data_source, timeframe, metric):
+    data_points = random.randint(5000, 50000)
+    avg_value = round(random.uniform(20, 80), 2)
+    peak = round(random.uniform(80, 100), 2)
+    anomalies = random.randint(0, 15)
+    
+    result = f"""### IoT Data Analysis Results
+
+**Data Source**: {data_source}  
+**Timeframe**: {timeframe}  
+**Metric**: {metric}
+
+📊 **Analysis Summary**:
+- Total Data Points: {data_points:,}
+- Average Value: {avg_value}
+- Peak Value: {peak}
+- Anomalies Detected: {anomalies}
+- Data Quality Score: {round(random.uniform(85, 99), 1)}%
+
+**Insights**: The IoT data shows {'consistent patterns' if anomalies < 5 else 'some irregularities'} over the {timeframe.lower()} period.
+
+---
+**Anktechsol** - IoT Analytics Platform  
+🔗 [Visit anktechsol.com](https://anktechsol.com)"""
+    return result
+
+with gr.Blocks(title="IoT Data Analyzer") as demo:
+    gr.Markdown("# 📊 IoT Data Analytics Platform")
+    gr.Markdown("Advanced IoT data insights - **Anktechsol**")
+    
+    with gr.Row():
+        with gr.Column():
+            source = gr.Dropdown(["Temperature Sensors", "Humidity Sensors", "Pressure Sensors", "Motion Detectors"], label="Data Source", value="Temperature Sensors")
+            time = gr.Radio(["Last Hour", "Last 24 Hours", "Last 7 Days", "Last 30 Days"], label="Timeframe", value="Last 24 Hours")
+            metric = gr.Dropdown(["Average", "Peak", "Trend Analysis", "Anomaly Detection"], label="Analysis Metric", value="Average")
+            btn = gr.Button("Analyze Data")
+        
+        with gr.Column():
+            output = gr.Markdown()
+    
+    btn.click(analyze_data, inputs=[source, time, metric], outputs=output)
+    
+    gr.Markdown("""---
+### Anktechsol - IoT Analytics Experts
+Comprehensive IoT data solutions. [Contact us](https://anktechsol.com)""")
+
+demo.launch()