debug

app.py

@@ -21,25 +21,42 @@ def calc_preds(coeffs, indeps):
         res = res @ l + consts[i]
         if i != n-1:
             res = F.relu(res)
-    # if torch.sigmoid(res) > 0.5:
-    return torch.sigmoid(res)
+    if torch.sigmoid(res) > 0.5:
+        return 'Real Job Post'
+    else:
+        return 'Fake Job Post'
+    # return torch.sigmoid(res)
+
+
+def preprocess_input(input_data):
+    df = pd.DataFrame([input_data], columns=indep_cols)
+
+    for column in df.columns:
+        if df[column].dtype == 'O':  # 'O' stands for object type (string)
+            df[column] = label_encoder.fit_transform(df[column])
+        else:
+            df[column] = df[column].astype(float)
+
+    t_indep = tensor(df[indep_cols].values, dtype=torch.float)
+    vals, indices = t_indep.max(dim=0)
+    t_indep = t_indep / vals
+
+    return t_indep
+
+def main(inputs):
+    t_indep = preprocess_input(inputs)
+    return calc_preds(coeffs, t_indep)
 
 def main(job_title, company_name, company_desc, job_desc,
               job_requirement, salary, location, employment_type,
               department):
-    df = pd.DataFrame(columns=indep_cols)
-    df.loc[len(df)] = [job_title, company_name, company_desc, job_desc,
+    inputs = [job_title, company_name, company_desc, job_desc,
               job_requirement, salary, location, employment_type,
               department]
 
-    for column in df.columns:
-        df[column] = label_encoder.fit_transform(df[column])
-
-    t_indep = tensor(df[indep_cols].values, dtype=torch.float)
-    vals,indices = t_indep.max(dim=0)
-    t_indep = t_indep / vals
-    # return calc_preds(coeffs, t_indep)
-    return df.loc
+    t_indep = preprocess_input(inputs)
+    
+    return calc_preds(coeffs, t_indep)
 
 iface = gr.Interface(
     fn=main,