- tweet generator and absa models added

app.py

@@ -1,11 +1,17 @@
 import streamlit as st
 from transformers import pipeline
+from constants import tweet_generator_prompt, absa_prompt
+
 
 # Initialize the model and tokenizer once, to avoid reloading them on each user interaction
 @st.cache_resource
 def load_model():
-    classifier = pipeline("text-classification", model="tweetpie/toxic-content-detector", return_all_scores=True)
-    return classifier
+    classification_pipe = pipeline(
+        "text-classification", model="tweetpie/toxic-content-detector", top_k=None)
+    absa_pipe = pipeline("text2text-generation", model="tweetpie/stance-aware-absa")
+    tweet_generation_pipe = pipeline("text2text-generation", model="tweetpie/stance-directed-tweet-generator")
+    return classification_pipe, absa_pipe, tweet_generation_pipe
+
 
 # Set up the title
 st.title("Toxic Content Classifier Dashboard")
@@ -13,7 +19,7 @@ st.title("Toxic Content Classifier Dashboard")
 # Top-level input for model selection
 model_selection = st.selectbox(
     "Select an ideology",
-    options=['alm', 'blm'],
+    options=['Left', 'Right'],
     index=0  # Default selection
 )
 
@@ -36,22 +42,47 @@ with col2:
 generate_button = st.button("Generate and classify toxicity")
 
 # Load the model
-classifier = load_model()
+classifier, absa, generator = load_model()
 
 # Process the input text and generate output
 if generate_button:
+    with st.spinner('Generating the tweet...'):
+        # Call the model with the aspects inputs
+        generated_tweet = generator(
+            tweet_generator_prompt.format(
+                ideology=model_selection.lower(),
+                pro_entities=pro_entities,
+                anti_entities=anti_entities,
+                neutral_entities=neutral_entities,
+                pro_aspects=pro_aspects,
+                anti_aspects=anti_aspects,
+                neutral_aspects=neutral_aspects
+            )
+        )
+
+    with st.spinner('Generating the Stance-Aware ABSA output...'):
+        # Call the model with the aspects inputs
+        absa_output = absa(absa_prompt.format(generated_tweet=generated_tweet[0]['generated_text']))
+        stances = [x.strip() for x in absa_output[0]['generated_text'].split(',')]
+
     with st.spinner('Classifying the toxicity...'):
         # Call the model with the input text
-        input_text = "I love you"
-        model_output = classifier(input_text)
+        model_output = classifier(generated_tweet[0]['generated_text'])
         output = model_output[0]
-        
-        # Displaying the input and model's output
-        st.write(f"Generated Tweet: {input_text}")
-        for i in range(3):
-            if output[i]['label'] == 'LABEL_0':
-                st.write(f"Non-Toxic Content: {output[i]['score']*100:.1f}%")
-            elif output[i]['label'] == 'LABEL_1':
-                st.write(f"Toxic Content: {output[i]['score']*100:.1f}%")
-            else:
-                continue
+
+# Displaying the input and model's output
+st.write(f"Generated Tweet: {generated_tweet[0]['generated_text']}")
+
+st.write("Stance-Aware ABSA Output:")
+st.write(f"{stances}")
+
+st.write("Toxicity Classifier Output:")
+for i in range(3):
+    if output[i]['label'] == 'LABEL_0':
+        # st.write(f"Non-Toxic Content: {output[i]['score']*100:.1f}%")
+        print(f"Non-Toxic Content: {output[i]['score']*100:.1f}%")
+    elif output[i]['label'] == 'LABEL_1':
+        # st.write(f"Toxic Content: {output[i]['score']*100:.1f}%")
+        print(f"Toxic Content: {output[i]['score']*100:.1f}%")
+    else:
+        continue

constants.py

@@ -0,0 +1,31 @@
+tweet_generator_prompt = """
+ideology: '{ideology}', pro entities: {pro_entities}, anti entities: {anti_entities}, 
+neutral entities: {neutral_entities} based on following aspects pro aspects: {pro_aspects}, 
+anti aspects: {anti_aspects}, neutral aspects: {neutral_aspects}
+"""
+
+absa_prompt = """
+Definition: The output will be the aspects (both implicit and explicit) and the aspects sentiment polarity. 
+In cases where there are no aspects the output should be noaspectterm:none.
+
+    Positive example 1-
+    input: I charge it at night and skip taking the cord with me because of the good battery life.
+    output: battery life:positive, 
+    Positive example 2-
+    input: I even got my teenage son one, because of the features that it offers, like, iChat, Photobooth, garage band and more!.
+    output: features:positive, iChat:positive, Photobooth:positive, garage band:positive
+    Negative example 1-
+    input: Speaking of the browser, it too has problems.
+    output: browser:negative
+    Negative example 2-
+    input: The keyboard is too slick.
+    output: keyboard:negative
+    Neutral example 1-
+    input: I took it back for an Asus and same thing- blue screen which required me to remove the battery to reset.
+    output: battery:neutral
+    Neutral example 2-
+    input: Nightly my computer defrags itself and runs a virus scan.
+    output: virus scan:neutral
+    Now complete the following example-
+    {generated_tweet}
+"""

requirements.txt

@@ -1,3 +1,4 @@
 streamlit==1.31.0
 transformers==4.38.2
-torch==2.2.1
+torch==2.2.1
+sentencepiece==0.2.0