Endpoint prepared

app.py

@@ -9,13 +9,16 @@ app = FastAPI()
 @app.post("/predict/")
 async def predict(id:str):
     try:
-        print("Peticion recibida")
-        tenant_list = rv.algo_start(int(id))
-        
-        
+        print("Request received")
+        tenant_json = rv.algo_start(int(id))
+        return tenant_json
+
+    
     except Exception as error:
         return {"prediction" : error}
     
 if __name__ =="__main__":
      
     uvicorn.run(app,host = "0.0.0.0", port=7860)
+
+# ASI HAREMOS LLAMADAS A LA API $ curl -X POST "https://luismidv-mlsystemtfg.hf.space/predict/?id=1"

resultview.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import similarity as sm
+import json
 
 def tenant_visualization(similarity_matrix, requested_tenants):
     #TODO VIEW COMPATIBILITY BETWEEN REQUESTED TENANTS
@@ -53,6 +54,26 @@ def algo_start(id):
     #sm.data_checking(dataframe)
     similarity_matrix = sm.encoder_matrix(dataframe, min_range = 0, max_range=100)
     tenant_list = tenant_inference(similarity_matrix, id,original_dataframe)
-    return tenant_list
-
+    json_convert(tenant_list)
     #tenant_visualization(similarity_matrix, [20,40,50,18,15])
+
+def json_convert(tenant_list):
+    #THE ALGO START FUNCTION COULD BE DOING THIS DIRECTLY, IS A POSSIBLE IMPROVEMENT IF THE WEB ISN'T WORKING FAST ENOUGH
+    
+    tenants_dict = {
+        str(i) : { "Names" : " ", "Age": " ", "Smoking": " ", "Email" : " ", "Compatibility" : " "} for i in range(4)
+    }
+
+    tenant_counter = 0
+    for tenant in tenant_list:
+        tenant_features = tenant[0]
+        tenant_compatibility = tenant[1]
+    
+        for feature in tenant_features.index:
+            tenants_dict[str(tenant_counter)][feature] = str(tenant_features[feature])
+
+        tenants_dict[str(tenant_counter)]["Compatibility"] = str(tenant_compatibility)    
+        tenant_counter += 1
+    
+    tenants_json = json.dumps(tenants_dict, indent = 4)
+    return tenants_json

try.py

@@ -0,0 +1,21 @@
+import requests
+import resultview as rv
+import json
+import pandas as pd
+
+url = "https://luismidv-mlsystemtfg.hf.space/predict/"
+params = {"id": "1"}
+
+
+
+
+
+
+
+
+    
+
+
+
+
+