- updates for webm generation

app.py

@@ -5,76 +5,58 @@ 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():
-#     start = time.time()
-#     classification_pipe = pipeline(
-#         "text-classification", model="tweetpie/toxic-content-detector", top_k=None)
-#     print(f"Time to load the classification model: {time.time() - start:.2f}s")
-#     start = time.time()
-#     absa_pipe = pipeline("text2text-generation", model="tweetpie/stance-aware-absa")
-#     print(f"Time to load the absa model: {time.time() - start:.2f}s")
-#     start = time.time()
-#     tweet_generation_pipe = pipeline("text2text-generation", model="tweetpie/stance-directed-tweet-generator")
-#     print(f"Time to load the tweet generation model: {time.time() - start:.2f}s")
-#     return classification_pipe, absa_pipe, tweet_generation_pipe
-
 # Set up the title
 st.title("Towards a Programmable Humanizing AI through Scalable Stance-Directed Architecture Dashboard")
 
-# Top-level input for model selection
-model_selection = st.selectbox(
-    "Select an ideology",
-    options=['Left', 'Right'],
-    index=0  # Default selection
-)
-
-# Layout for entities and aspects inputs
-col1, col2, col3 = st.columns(3)  # Adjusted to include a third column
+# Adjust the layout for wider containers
+st.set_page_config(layout="wide")
 
+# Container for ideology selection spanning across first two columns
+col1, col2, _ = st.columns([2, 2, 4])  # Adjust the ratios as needed for better appearance
 with col1:
+    model_selection = st.selectbox(
+        "Select an ideology",
+        options=['Left', 'Right'],
+        index=0  # Default selection
+    )
+
+# Layout for entities and aspects inputs
+with col2:
     st.header("Entities")
     pro_entities = st.text_input("Pro Entities", help="Enter pro entities separated by commas")
     anti_entities = st.text_input("Anti Entities", help="Enter anti entities separated by commas")
     neutral_entities = st.text_input("Neutral Entities", help="Enter neutral entities separated by commas")
 
-with col2:
+col3, col4 = st.columns([1, 3])  # Splitting the remaining space for aspects input and outputs
+
+with col3:
     st.header("Aspects")
     pro_aspects = st.text_input("Pro Aspects", help="Enter pro aspects separated by commas")
     anti_aspects = st.text_input("Anti Aspects", help="Enter anti aspects separated by commas")
     neutral_aspects = st.text_input("Neutral Aspects", help="Enter neutral aspects separated by commas")
 
-# Generate button
+# Generate button (placed outside the columns so it spans the full width)
 generate_button = st.button("Generate tweet and classify toxicity")
 
-# Load the model
+# Load the model (commented out, assuming model loading is handled elsewhere)
 # classifier, absa, generator = load_model()
 
 # Process the input text and generate output
 if generate_button:
-    with col3:  # This block is for displaying outputs in the third column
+    with col4:  # This block is for displaying outputs in the wider column
         with st.spinner('Generating the tweet...'):
-            # Call the model with the aspects inputs
-            prompt = 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
-            )
-            time.sleep(5)
-            generated_tweet = [{"generated_text": "the agricultural sector is the single biggest recipient of migrants workers rights groups argue . nearly 90 % of those who come to the us are denied employment due to discriminatory employment laws and safety standards ."}]
+            # Example placeholders for the generated outputs
+            time.sleep(5)  # Simulating delay
+            generated_tweet = [{
+                                   "generated_text": "the agricultural sector is the single biggest recipient of migrants workers rights groups argue . nearly 90 % of those who come to the us are denied employment due to discriminatory employment laws and safety standards ."}]
 
             # Displaying the input and model's output
             st.write(f"Generated Tweet: {generated_tweet[0]['generated_text']}")
 
         with st.spinner('Generating the Stance-Aware ABSA output...'):
-            time.sleep(3)
+            time.sleep(3)  # Simulating delay
             absa_output = [{'generated_text': 'migrants:positive, rights:positive, laws:positive, safety:positive'}]
-            print("ABSA Output: ", absa_output)
+
             stances = [x.strip() for x in absa_output[0]['generated_text'].split(',')]
             stances = [{
                 'Aspect': x.split(':')[0],
@@ -86,15 +68,15 @@ if generate_button:
             st.table(stances_df)
 
         with st.spinner('Classifying the toxicity...'):
-            # model_output = classifier(generated_tweet[0]['generated_text'])
-            time.sleep(2)
-            model_output = [[{'label': 'LABEL_0', 'score': 0.9999998807907104}, {'label': 'LABEL_1', 'score': 1.1919785395889282e-07}, {'label': 'LABEL_2', 'score': 1.1919785395889282e-07}]]
+            time.sleep(2)  # Simulating delay
+            model_output = [[{'label': 'LABEL_0', 'score': 0.9999998807907104},
+                             {'label': 'LABEL_1', 'score': 1.1919785395889282e-07}]]
             output = model_output[0]
 
             st.write("Toxicity Classifier Output:")
-            for i in range(3):
+            for i in range(len(output)):
                 if output[i]['label'] == 'LABEL_0':
-                    st.write(f"Non-Toxic Content: {output[i]['score']*100:.1f}%")
+                    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: