Update app.py

app.py

@@ -1,35 +1,35 @@
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 import gradio as gr
 
-# Load T0pp (instruction-tuned)
-model_name = "tencent/Hunyuan-A13B-Instruct"
+# Use lightweight, instruction-tuned model
+model_name = "google/flan-t5-base"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
 
-# Debater personas
+# Personas
 personas = {
-    "🟢 Optimist": "Give a hopeful, positive opinion with 2 reasons.",
-    "🔴 Pessimist": "Criticize the topic and point out 2 problems.",
-    "🟡 Neutral": "Give a fair and balanced view with pros and cons."
+    "🟢 Optimist": "Provide a positive and hopeful opinion, mentioning 2 benefits.",
+    "🔴 Pessimist": "Provide a critical opinion, mentioning 2 drawbacks.",
+    "🟡 Neutral": "Provide a balanced perspective, listing pros and cons."
 }
 
 # Debate function
 def generate_debate(topic):
-    responses = {}
+    results = []
     for label, instruction in personas.items():
-        prompt = f"You are a debater. {instruction}\nDebate Topic: {topic}"
-        result = pipe(prompt, max_new_tokens=150, temperature=0.7)[0]['generated_text']
-        responses[label] = result.strip()
-    return "\n\n".join([f"**{k}**:\n{v}" for k, v in responses.items()])
+        prompt = f"You are a debater. Topic: '{topic}'. {instruction}"
+        response = pipe(prompt, max_new_tokens=120, temperature=0.7)[0]['generated_text'].strip()
+        results.append(f"### {label}\n{response}")
+    return "\n\n".join(results)
 
-# Gradio UI
+# Gradio Interface
 demo = gr.Interface(
     fn=generate_debate,
     inputs=gr.Textbox(label="Debate Topic"),
     outputs=gr.Markdown(),
-    title="🎤 Multi-Agent Debate Simulator (T0pp)",
-    description="Simulates a debate with multiple perspectives using the instruction-tuned BigScience T0pp model on Hugging Face 🤗."
+    title="🎙️ Multi-Agent Debate Simulator",
+    description="Debate from 3 perspectives (Optimist, Pessimist, Neutral) using FLAN-T5-Base."
 )
 
 demo.launch()