Update app.py

app.py

@@ -1,43 +1,33 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 import gradio as gr
 
-# Load Mistral-7B with 8-bit quantization (saves memory!)
-model_id = "mistralai/Mistral-7B-Instruct-v0.3"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
+model_name = "facebook/mbart-large-50-many-to-many-mmt"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
 
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    device_map="auto",         # Automatically assign layers to available GPU/CPU
-    load_in_8bit=True,         # Use 8-bit quantization
-    torch_dtype=torch.float16  # Reduce precision to save memory
-)
-
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-# Viewpoints
+# Role-based prompt
 personas = {
-    "🟢 Optimist": "Respond with hope and positivity",
-    "🔴 Pessimist": "Respond critically and negatively",
-    "🟡 Neutral": "Respond with a balanced, unbiased tone"
+    "🟢 Optimist": "You are an optimist. Defend the topic with positivity. Give 2 reasons.",
+    "🔴 Pessimist": "You are a pessimist. Criticize the topic. Mention 2 problems.",
+    "🟡 Neutral": "You are a neutral thinker. Present pros and cons fairly."
 }
 
-# Generate debate
 def generate_debate(topic):
     responses = {}
-    for label, style in personas.items():
-        prompt = f"[INST] You are a debater. {style}. Topic: '{topic}'. Give a short opinion. [/INST]"
-        result = pipe(prompt, max_new_tokens=150, temperature=0.7, do_sample=True)[0]["generated_text"]
+    for label, instruction in personas.items():
+        prompt = f"{instruction}\nDebate Topic: {topic}"
+        result = pipe(prompt, max_new_tokens=120, do_sample=True, temperature=0.7)[0]["generated_text"]
         responses[label] = result.strip()
     return "\n\n".join([f"**{k}**: {v}" for k, v in responses.items()])
 
-# Gradio app
+# Gradio UI
 demo = gr.Interface(
     fn=generate_debate,
     inputs=gr.Textbox(label="Enter a Debate Topic"),
     outputs=gr.Markdown(),
-    title="🧠 Multi-Agent Debate Simulator (Mistral 7B)",
-    description="Debate with different perspectives using the Mistral-7B-Instruct model (quantized)."
+    title="🤖 Debate Club: Multi-Agent Edition",
+    description="A mini debate simulation using different perspectives powered by MBART. Runs on free-tier CPU."
 )
 
 demo.launch()