COmmit on endpoint

app.py

@@ -6,10 +6,16 @@ import uvicorn
 app = FastAPI()
 
 
-app.post("/predict/")
-async def predict(id):
-    tenant_list = rv.algo_start(id)
-    return tenant_list
+@app.get("/predict/")
+async def predict(id:str):
+    try:
+        print("Peticion recibida")
+        tenant_list = rv.algo_start(int(id))
+        
+        
+    except Exception as error:
+        return {"prediction" : error}
     
 if __name__ =="__main__":
+     
     uvicorn.run(app,host = "127.0.0.1", port=7860)

resultview.py

@@ -1,4 +1,5 @@
 import pandas as pd
+import similarity as sm
 
 def tenant_visualization(similarity_matrix, requested_tenants):
     #TODO VIEW COMPATIBILITY BETWEEN REQUESTED TENANTS
@@ -25,14 +26,10 @@ def tenant_visualization(similarity_matrix, requested_tenants):
         
         case 4:
             most_compatible = tenant_lines.sort_values(ascending = False)
-            most_compatible = dataframe.loc[requested_tenants]
+        
             print(f"Most compatible tenants registers\n {most_compatible}")
 
-def view_kmeans_results(results,cluster_center): 
-    # TODO FUNCTION TO VIEW KMEANS RESULTS
-    print(f"Starting kmeans viewing \n Cluster length: {results.shape}")
-    plt.scatter(results,results)
-    plt.show()
+
 
 def tenant_inference(similarity_matrix, requested_tenants,dataframe):
     #TODO THIS FUNCTION IS THE ONE USED DURING INFERENCE TIME THE MODEL WILL CALCULATE THE 4 TENANTS WITH THE HIGHER COMPATIBILITY

similarity.py

@@ -3,7 +3,7 @@ from sklearn.preprocessing import OneHotEncoder
 import pandas as pd
 
 def data_preparing():
-    original_dataframe = pd.read_csv('./MLSystem/data/users_dataframe.csv')
+    original_dataframe = pd.read_csv('./data/users_dataframe.csv')
     columns = ['Age', 'Worktimes', 'Schedules', 'Studies level', 'Pets', 'Cooking', 'Sport', 'Smoking', 'Organized']
     dataframe = original_dataframe[columns]
     return dataframe, original_dataframe