Update app.py

app.py

@@ -59,19 +59,40 @@ def predict_for_example(sentence, tags, model):
 
     return model.predict_tags(x)
 
-def find_chunks(sentence, preds):
-    chunks = []
-    chunk = []
-    for i in range(len(preds)):
-        if preds[i] == 1:
-            if len(chunk) > 1:
-                chunks.append(" ".join(chunk))
-            chunk = [sentence[i]]
-        elif preds[i] == 0 and chunk:
-            chunk.append(sentence[i])
-    if len(chunk) > 1:
-        chunks.append(" ".join(chunk))
-    return chunks
+def get_noun_chunks(tokens, pos_tags, chunk_tags):
+    
+
+    sequences = []
+    noun_chunks = []
+    noun_chunks_pos_tags = []
+    noun_chunks_tags = []
+    start = None
+    i = 0
+
+    while i < len(chunk_tags):
+        
+        if chunk_tags[i] == 1:
+            start = i
+            if pos_tags[i] == 'NN':
+                noun_chunks.append([tokens[i]])
+                noun_chunks_pos_tags.append([pos_tags[i]])
+                noun_chunks_tags.append([chunk_tags[i]])
+
+        
+        while i+1<len(chunk_tags) and chunk_tags[i+1] == 0 and (start is not None):
+            i+=1
+
+        if  (start is not None) and i >  start:
+            noun_chunks.append(tokens[start:i+1])
+            noun_chunks_pos_tags.append(pos_tags[start:i+1])
+            noun_chunks_tags.append(chunk_tags[start:i+1])
+            start =None
+        
+        i+=1
+    noun_chunks = [" ".join(i) for i in noun_chunks]
+    sequences = [noun_chunks,noun_chunks_pos_tags, noun_chunks_tags]
+    return sequences
+
 
 
 model2 = PRNN()  # Instantiate a model
@@ -94,13 +115,37 @@ P_best4, W_best4 = process_CVresults(CVresults_dict=model_dict4, summarize=False
 model4.params = P_best4
 model4.w = W_best4
 
+model1 = PRNN()  # Instantiate a model
+
+# Loading the dictionary from the file using pickle
+with open('CVresults_data.pkl', 'rb') as f:
+    model_dict1 = pickle.load(f)
+
+P_best1, W_best1 = process_CVresults(CVresults_dict=model_dict1, summarize=False)
+model1.params = P_best1
+model1.w = W_best1
+
+model3 = PRNN()  # Instantiate a model
+
+# Loading the dictionary from the file using pickle
+with open('CVresults_data_sigmoid.pkl', 'rb') as f:
+    model_dict3 = pickle.load(f)
+
+P_best3, W_best3 = process_CVresults(CVresults_dict=model_dict3, summarize=False)
+model3.params = P_best3
+model3.w = W_best3
+
+
+
 def demo_(sentence):
   sentence, tags = tokens_and_tags(sentence)
+  preds1=predict_for_example(sentence=sentence, tags=tags, model=model1)
+  preds3=predict_for_example(sentence=sentence, tags=tags, model=model3)
   preds2=predict_for_example(sentence=sentence, tags=tags, model=model2)
   preds4=predict_for_example(sentence=sentence, tags=tags, model=model4)
-  return str(preds2),str(preds4),str(find_chunks(sentence=sentence, preds=preds2)),str(find_chunks(sentence=sentence, preds=preds4))
+  return "predicted labels:\t"str(preds2)+"\n"+"predicted Noun chunks \t"str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds2)),"predicted labels:\t"str(preds4)+"\n"+"predicted Noun chunks \t"str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds4)),"predicted labels:\t"str(preds1)+"\n"+"predicted Noun chunks \t"str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds1)),"predicted labels:\t"str(preds3)+"\n"+"predicted Noun chunks \t"str(get_noun_chunks(sentence=sentence, tags=tags,preds=preds3))tags
 
 title="POS-Tagged Corpus Analysis: Training a Recurrent Perceptron for Noun Chunk Identification"
-demo = gr.Interface(fn=demo_, inputs=gr.Textbox(label="sentence for which you want noun chunks"), outputs=[gr.Textbox(label="predicted chunk label with step activation function"),gr.Textbox(label="predicted chunk label with sigmoid activation function"),gr.Textbox(label="predicted Noun chunk with step activation function"),gr.Textbox(label="predicted Noun chunk with sigmoid activation function")],title=title)
+demo = gr.Interface(fn=demo_, inputs=gr.Textbox(label="sentence for which you want noun chunks",lines=1, interactive=True, show_copy_button=True), outputs=[gr.Textbox(label="prediction on conditioned data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on conditioned data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on all data with step activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="prediction on whole data with sigmoid activation function",lines=2, interactive=True, show_copy_button=True),gr.Textbox(label="pos tag label given by nltk library",lines=1, interactive=True, show_copy_button=True)],title=title)
 
 demo.launch(share=True)