Update app.py

app.py

@@ -1,15 +1,30 @@
-import numpy as np
+import streamlit as st
+import pandas as pd
 
-def image_classifier(inp):
-  confidence_scores = np.random.rand(3)
-  confidence_scores/= np.sum(confidence_scores)
-  classes= ['cable','case', 'cpu']
-  result= {classes[i]: confidence_scores[i] for i in range(3)}
-  return result
+# Load data
+data = pd.read_csv("computer_parts_data.csv")
 
-import gradio as gr
-demo = gr.Interface(fn=image_classifier, inputs = "image", outputs = "label")
-demo.launch()
+# Streamlit app UI
+def main():
+    st.title("Computer Parts Identifier")
 
+    # Sidebar for user input
+    st.sidebar.header("Filter Options")
+    brand = st.sidebar.text_input("Brand")
+    price_range = st.sidebar.slider("Price Range", min_value=0, max_value=2000, value=(0, 2000))
+    # Add more filter options as needed
+
+    # Filter data based on user input
+    filtered_data = data[(data['Brand'].str.contains(brand, na=False)) & 
+                         (data['Price'] >= price_range[0]) & 
+                         (data['Price'] <= price_range[1])]
+    # Add more filtering conditions as needed
+
+    # Display filtered results
+    st.subheader("Matching Computer Parts")
+    st.write(filtered_data)
+
+if __name__ == "__main__":
+    main()