Create app.py

app.py

@@ -0,0 +1,99 @@
+import csv
+import spacy 
+import glob
+import datetime
+import pandas as pd
+import gradio as gr
+from transformers import pipeline
+from huggingface_hub import hf_hub_download
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+models = {
+          'en': 'facebook/bart-large-mnli'
+         }
+
+hypothesis_templates = {
+                        'en': 'This example is {}.'
+                       }
+
+classifiers = {'en': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['en'],
+                              model=models['en'])
+              }
+
+nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
+tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')
+labels=["contradicts_hypothesis","Neutral","Entails_hypothesis"]
+
+def prep_examples():
+    example_text1 = "EMI can be skipped"
+    example_labels1 = "EMI can be skipped" #"Entails Hypothisis"
+
+    example_text2 = "minimum package guranteed"
+    example_labels2 = "minimum package guranteed" #"Entails Hypothisis"
+
+    example_text3 = "100% placement gurantee"
+    example_labels3 = "100% placement gurantee" #"Entails Hypothisis"
+
+    #example_text1 = "EMI can not be skipped"
+    #example_labels1 = "contradicts_hypothesis"
+
+
+    examples = [
+        [example_text1, example_labels1, False],
+        [example_text2, example_labels2, False],
+        [example_text3, example_labels3, False]]
+
+    return examples
+
+
+def inference_hypothesis(premise,hypothesis,labels):
+    x = tokenizer.encode(premise, hypothesis, return_tensors='pt',truncation_strategy='only_first')
+    logits = nli_model(x.to("cpu"))[0]
+    entail_contradiction_logits = logits[:,[0,1,2]]
+    probs = entail_contradiction_logits.softmax(dim=1)
+    return premise,hypothesis,labels[probs.argmax()],entail_contradiction_logits
+
+def sequence_to_classify(sequence, hypothesis_df, multi_label):
+    hypothesis_df=pd.read_csv(hypothesis_df.name)
+    lang = 'en'
+    classifier = classifiers[lang]
+    inference_output={}
+    label_clean = str(labels).split(";;")
+    for i,keyword in enumerate(hypothesis_df.filtering_keyword.tolist()):
+      if keyword.lower() in sequence.lower():
+        output = inference_hypothesis(sequence, hypothesis_df.hypothesis.tolist()[i],labels)
+        if output[2]==hypothesis_df.expected_inference.tolist()[i]:
+                      inference_output[output[0]]={"hypothesis":output[1],"label":output[2],"score":output[3]}
+                      #inference_output.append(output)
+    return inference_output
+    predicted_labels = response['labels']
+    predicted_scores = response['scores']
+    clean_output = {idx: float(predicted_scores.pop(0)) for idx in predicted_labels}
+    print("Date:{}, Sequence:{}, Labels: {}".format(
+        str(datetime.datetime.now()),
+        sequence,
+        predicted_labels))
+
+    if not multi_label:
+         top_label_key = list(clean_output.keys())[0]
+         clean_output = {top_label_key: clean_output[top_label_key]}
+    return clean_output
+def csv_to_df(file):
+  return pd.read_csv(file)
+  
+def csv_to_json(df):
+  return df.to_json(orient="records")
+
+iface = gr.Interface(
+    title="Sales Call Analysis AI - NS AI LABS",
+    description="Off-the-shelf NLP classifier with no domain or task-specific training.",
+    fn=sequence_to_classify,
+    inputs=[gr.inputs.Textbox(lines=10,
+        label="Please enter the text you would like to classify...",
+        placeholder="Text here..."),
+        gr.inputs.File(),
+        gr.inputs.Radio(choices=[False, True],
+        label="Multi-label?")],
+    outputs=gr.outputs.Textbox())
+
+iface.launch()