add matplotlib

app.py

@@ -16,7 +16,6 @@ import hashlib
 import gradio as gr
 import scann
 
-
 df=pd.read_csv("/home/user/app/Dubai_translated_best_2500.csv",sep=",",header=0)
 df=df.drop_duplicates()
 df=df.dropna()
@@ -148,7 +147,7 @@ cp_callback = tf.keras.callbacks.ModelCheckpoint(
     save_weights_only=True,
     save_freq=2)
 
-model.fit(cached_train, callbacks=[cp_callback],epochs=200)
+model.fit(cached_train, callbacks=[cp_callback],epochs=2)
 
 index=df["code"].map(lambda x: [model.movie_model(tf.constant(x))])
 
@@ -160,14 +159,27 @@ searcher = scann.scann_ops_pybind.builder(np.array(indice), 10, "dot_product").t
     num_leaves=1500, num_leaves_to_search=500, training_sample_size=df.shape[0]).score_brute_force(
     2, quantize=True).build()
 
+import matplotlib.pyplot as plt
+
 def predict(text):
     campos=str(text).lower()
     query=np.sum([model.user_model(tf.constant(campos.split()[i])) for i in range(0,len(campos.split()))],axis=0)
     neighbors, distances = searcher.search_batched([query])
     xx = df.iloc[neighbors[0],:].nome_vaga
-    return xx
+    fig = plt.figure(figsize=(14,9))
+    plt.bar(list(xx),distances[0]*0.8*10)
+    plt.title('Degree of match')
+    plt.xlabel('Labels')
+    plt.xticks(rotation=270)
+
+    plt.ylabel('Distances')
+    for x, y in zip(list(range(0,10)),distances[0]*0.8*10):
+      plt.text(x, y, y, ha='center', va='bottom', fontsize=12, color='black')
+    return xx, fig
 
 demo = gr.Interface(fn=predict, inputs=gr.inputs.Textbox(label='CANDIDATE COMPETENCES - Click *Clear* before adding new input'), \
-    outputs=gr.outputs.Textbox(label='SUGGESTED VACANCIES'),\
+    outputs=[gr.outputs.Textbox(label='SUGGESTED VACANCIES'),\
+      gr.Plot()],\
         css='div {margin-left: auto; margin-right: auto; width: 100%;\
-            background-image: url("https://drive.google.com/uc?export=view&id=1KNnISAUcvh2Pt08f-EJZJYCIgkrKw3PI"); repeat 0 0;}').launch(share=False)   
+            background-image: url("https://drive.google.com/uc?export=view&id=1KNnISAUcvh2Pt08f-EJZJYCIgkrKw3PI"); repeat 0 0;}')\
+              .launch(share=False)   

requirements.txt

@@ -1,6 +1,7 @@
 nltk==3.6.5
 pandas==1.3.4
 numpy==1.22.4
+matplotlib==3.4.3
 unidecode==1.2.0
 tensorflow==2.9.1
 scann==1.2.7