Add application file

app.py

@@ -1,49 +1,16 @@
-!pip install scikit-learn
-import pandas as pd
-from sklearn.preprocessing import StandardScaler
-from sklearn.ensemble import RandomForestClassifier
 import gradio as gr
-import numpy as np
 
-# Generate random sensor data
-np.random.seed(42)
-data = pd.DataFrame({
-    "Temperature": np.random.randint(0, 100, 100),
-    "Humidity": np.random.randint(0, 100, 100),
-    "Pressure": np.random.randint(0, 100, 100),
-    "Vibration": np.random.randint(0, 100, 100),
-    "Alert": np.random.randint(0, 2, 100)
-})
-
-# Split the data into features and target
-X = data.drop(columns=["Alert"])
-y = data["Alert"]
-
-# Scale the features using the StandardScaler
-scaler = StandardScaler()
-X_scaled = scaler.fit_transform(X)
-
-# Train a random forest classifier on the scaled data
-clf = RandomForestClassifier(random_state=42)
-clf.fit(X_scaled, y)
-
-# Define the input and output components for Gradio
-input_components = [
-    gr.inputs.Slider(minimum=0, maximum=100, default=50, label="Temperature"),
-    gr.inputs.Slider(minimum=0, maximum=100, default=50, label="Humidity"),
-    gr.inputs.Slider(minimum=0, maximum=100, default=50, label="Pressure"),
-    gr.inputs.Slider(minimum=0, maximum=100, default=50, label="Vibration"),
-]
-
-output_component = gr.outputs.Textbox(label="Alert")
-
-# Define the predict function to make the prediction using the model
-def predict(temp, humidity, pressure, vibration):
-    input_data = [[temp, humidity, pressure, vibration]]
-    input_data_scaled = scaler.transform(input_data)
-    prediction = clf.predict(input_data_scaled)[0]
-    return "Alert!" if prediction == 1 else "No alert"
-
-# Create the Gradio interface and run the app
-interface = gr.Interface(predict, inputs=input_components, outputs=output_component, title="5G IoT Alert System")
-interface.launch()
+def walking_assistant(voice_command):
+    if voice_command.lower() == 'stop':
+        return "Stopping"
+    elif voice_command.lower() == 'forward':
+        return "Moving Forward"
+    elif voice_command.lower() == 'left':
+        return "Turning Left"
+    elif voice_command.lower() == 'right':
+        return "Turning Right"
+    else:
+        return "Invalid Command"
+
+iface = gr.Interface(fn=walking_assistant, inputs="text", outputs="text", title="Walking Assistant for the Visually Impaired")
+iface.launch()