Update app.py

app.py

@@ -60,25 +60,8 @@ preprocessor = joblib.load("preprocessor.pkl")
 scaler = joblib.load("scaler.pkl")
 label_encoders = joblib.load("label_encoders.pkl")
 
-def predict_lifecycle(category, product_name, price, rating, num_reviews, stock_quantity, discount):
-    input_df = pd.DataFrame([[category, product_name, price, rating, num_reviews, stock_quantity, discount]],
-                            columns=["Category", "ProductName", "Price", "Rating", "NumReviews", "StockQuantity", "Discount"])
-    processed_input = preprocessor.transform(input_df)
-    prediction = product_model.predict(processed_input)[0]
-    return f"Predicted Product Lifecycle: {round(prediction, 2)} years"
-
-def predict_price(product_name, category, base_price, competitor_price, demand, stock, reviews, rating, season, discount):
-    category = label_encoders["Category"].transform([category])[0]
-    demand = label_encoders["Demand"].transform([demand])[0]
-    season = label_encoders["Season"].transform([season])[0]
-    product_name = label_encoders["Product Name"].transform([product_name])[0]
-    features = np.array([base_price, competitor_price, stock, reviews, rating, discount]).reshape(1, -1)
-    scaled_features = scaler.transform(features)
-    final_features = np.concatenate((scaled_features.flatten(), [category, demand, season, product_name])).reshape(1, -1)
-    return f"Optimal Price: ₹{round(dynamic_pricing_model.predict(final_features)[0], 2)}"
-
 def recommend_products(category):
-    recommended = recommendation_knn.kneighbors(category, return_distance=False)
+    recommended = recommendation_knn.kneighbors([[category]], return_distance=False)
     return recommended.tolist()
 
 def generate_dashboard():
@@ -92,6 +75,23 @@ def generate_dashboard():
 # Gradio UI
 with gr.Blocks() as app:
     gr.Markdown("# 🔄 Circular Economy Marketplace")
+    with gr.Tab("Login / Register"):
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### Register")
+                reg_username = gr.Textbox(label="Username")
+                reg_password = gr.Textbox(label="Password", type="password")
+                reg_button = gr.Button("Register")
+                reg_output = gr.Textbox()
+                reg_button.click(register, inputs=[reg_username, reg_password], outputs=reg_output)
+            with gr.Column():
+                gr.Markdown("### Login")
+                log_username = gr.Textbox(label="Username")
+                log_password = gr.Textbox(label="Password", type="password")
+                log_button = gr.Button("Login")
+                log_output = gr.Textbox()
+                log_button.click(login, inputs=[log_username, log_password], outputs=log_output)
+    
     with gr.Tab("Product Lifecycle Prediction"):
         gr.Interface(
             fn=predict_lifecycle,
@@ -106,6 +106,7 @@ with gr.Blocks() as app:
             ],
             outputs=gr.Textbox(label="Prediction")
         )
+    
     with gr.Tab("Dynamic Pricing"):
         gr.Interface(
             fn=predict_price,
@@ -123,6 +124,14 @@ with gr.Blocks() as app:
             ],
             outputs=gr.Textbox(label="Predicted Price")
         )
+    
+    with gr.Tab("Product Recommendations"):
+        gr.Interface(
+            fn=recommend_products,
+            inputs=[gr.Dropdown(["Electronics", "Plastic", "Metal", "Wood", "Composite"], label="Category")],
+            outputs=gr.Textbox(label="Recommended Products")
+        )
+    
     with gr.Tab("Analytics Dashboard"):
         gr.Interface(fn=generate_dashboard, inputs=[], outputs=[gr.Plot(), gr.Plot(), gr.Plot(), gr.Plot()])
 app.launch(share=True)