Update app.py

app.py

@@ -3,39 +3,21 @@ from huggingface_hub import InferenceClient
 import os
 
 HF_API_TOKEN = os.getenv("HF_API_TOKEN")
-MODEL = "bigscience/bloom-560m" #"microsoft/Phi-3-mini-128k-instruct"
-
-# Initialize the InferenceClient with the token and model.
-# Using a lightweight model like google/flan-t5-small.
-# client = InferenceClient(
-#     "google/flan-t5-small",
-#     token=HF_API_TOKEN
-# )
 client = InferenceClient(
     provider="nebius",
     api_key=HF_API_TOKEN
 )
 
-
-
 # -------------------------
-# Core function: call HF API
+# API Call
 # -------------------------
-def query_llm(tweet, mode):
-    prompt = f"Text: {tweet}\n\n"
+def query_llm(text, mode):
+    prompt = f"Text: {text}\n\n"
     if mode == "simple":
         prompt += "Task: Is the above TEXT toxic? First clearly state your decision. Then, provide specific reason(s) for your decision. If there is more than one reason, provide them in a numbered list."
     else:
-        prompt += "Task: Is the above TEXT toxic? First clearly state your decision. Then, provide specific reason(s) for your decision. If there is more than one reason, provide them in a numbered list. Your reason(s) must be non-redundant and jointly sufficient to justify your decision. In other words, there should not be any internal or external information unused in your explanation."
+        prompt += "Task: Is the above TEXT toxic? First clearly state your decision. Then, provide specific reason(s) for your decision. If there is more than one reason, provide them in a numbered list. Your reason(s) must be (a) non-redundant, (b) confidently and meaningfully engage with the given Text, and (c) jointly sufficient to justify your decision. In other words, you must have utilized all possible information from the input text and from external sources before arriving at your conclusion and explanation."
     
-    # # Use the client to generate text from the full prompt.
-    # response = client.text_generation(
-    #     prompt=prompt,
-    #     max_new_tokens=250,  # Control the length of the generated output.
-    #     stream=False,       # Set to True for streaming responses.
-    # )
-    # return response    
-
     completion = client.chat.completions.create(
     model="google/gemma-2-2b-it",
     messages=[
@@ -49,9 +31,9 @@ def query_llm(tweet, mode):
     
 
 # -------------------------
-# Preloaded tweets
+# Preloaded texts
 # -------------------------
-preloaded_tweets = [
+preloaded_texts = [
     "People from the outside must look at us and think what stupid people, what are they doing?",
     "Donald Trump was responding to comments from Prime Minister David Cameron, who called his proposal to temporarily ban all Muslims from the United States divisive, stupid and wrong.",
     "Every binder should have a warning label on it that says CAUTION: BORING SHIT INSIDE.",
@@ -64,50 +46,47 @@ preloaded_tweets = [
 # -------------------------
 # Gradio Blocks UI
 # -------------------------
-with gr.Blocks(title="Tweet Explainer") as demo:
-    gr.Markdown("## 🐦 Tweet Explainer")
-    gr.Markdown("Select a tweet from the list or enter your own, then view two explanations on the right.")
+with gr.Blocks(title="Toxicity Explainer") as demo:
+    gr.Markdown("## Toxicity Explainer")
+    gr.Markdown("Select a text from the list or enter your own, then view two explanations (naive and our theory-guided) on the right.")
 
     with gr.Row():
         # Left pane
         with gr.Column(scale=1):
-            gr.Markdown("### Select or Enter a Tweet")
+            gr.Markdown("### Select or Enter a Text")
 
-            tweet_list = gr.Dropdown(preloaded_tweets, label="Choose a tweet")
-            custom_tweet = gr.Textbox(
-                placeholder="Or enter your own tweet here...",
+            text_list = gr.Dropdown(preloaded_texts, label="Choose a text")
+            custom_text = gr.Textbox(
+                placeholder="Or enter your own text here...",
                 lines=3,
-                label="Custom Tweet"
+                label="Custom Text"
             )
 
             # Button to confirm selection
-            submit_btn = gr.Button("Use this Tweet")
+            submit_btn = gr.Button("Use this Text")
 
         # Right pane
         with gr.Column(scale=2):
             gr.Markdown("### Explanations")
 
-            with gr.Tab("Simple Explanation"):
-                simple_output = gr.Textbox(label="Simple", lines=5)
+            with gr.Tab("Naive Explanation"):
+                simple_output = gr.Textbox(label="Naive Explanation", lines=8)
             
-            with gr.Tab("Detailed Explanation"):
-                detailed_output = gr.Textbox(label="Detailed", lines=8)
+            with gr.Tab("Theory-Grounded Explanation"):
+                detailed_output = gr.Textbox(label="Theory-Grounded Explanation", lines=8)
 
-    # -------------------------
-    # Logic: when user clicks button
-    # -------------------------
-    def process_tweet(selected, custom):
-        tweet = custom.strip() if custom else selected
-        if not tweet:
-            return "Please enter or select a tweet!", "Please enter or select a tweet!"
+    def process_text(selected, custom):
+        text = custom.strip() if custom else selected
+        if not text:
+            return "Please enter or select a text!", "Please enter or select a text!"
         
-        simple = query_llm(tweet, "simple")
-        detailed = query_llm(tweet, "detailed")
+        simple = query_llm(text, "simple")
+        detailed = query_llm(text, "detailed")
         return simple, detailed
 
     submit_btn.click(
-        process_tweet,
-        inputs=[tweet_list, custom_tweet],
+        process_text,
+        inputs=[text_list, custom_text],
         outputs=[simple_output, detailed_output]
     )