Update app.py

app.py

@@ -1,43 +1,36 @@
 import gradio as gr
-from transformers import GPT2LMHeadModel, GPT2Tokenizer, BertForMaskedLM, BertTokenizer
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-# Load GPT-2 for the initial argument
-model1 = GPT2LMHeadModel.from_pretrained("gpt2")
-tokenizer1 = GPT2Tokenizer.from_pretrained("gpt2")
+# Load GPT-2 for both initial argument and counter-argument
+model = GPT2LMHeadModel.from_pretrained("gpt2-medium")
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium")
 
-# Load BERT for the counter-argument
-model2 = BertForMaskedLM.from_pretrained("bert-base-uncased")
-tokenizer2 = BertTokenizer.from_pretrained("bert-base-uncased")
-
-def generate_gpt2_argument(query, max_length=200, temperature=0.7):
+def generate_argument(prompt, max_length=200, temperature=0.7):
     try:
-        prompt = f"Provide a logical explanation for the following topic: {query}\n\nExplanation:"
-        inputs = tokenizer1.encode(prompt, return_tensors="pt")
-        outputs = model1.generate(
+        inputs = tokenizer.encode(prompt, return_tensors="pt")
+        outputs = model.generate(
             inputs,
             max_length=max_length,
             temperature=temperature,
             num_return_sequences=1,
             do_sample=True,
-            pad_token_id=tokenizer1.eos_token_id
+            pad_token_id=tokenizer.eos_token_id
         )
-        return tokenizer1.decode(outputs[0], skip_special_tokens=True).replace(prompt, "").strip()
+        return tokenizer.decode(outputs[0], skip_special_tokens=True).replace(prompt, "").strip()
     except Exception as e:
         return f"An error occurred: {str(e)}"
 
-def generate_bert_argument(query, initial_argument, max_length=200):
-    try:
-        prompt = f"Topic: {query}. Initial argument: {initial_argument}. Counter-argument:"
-        inputs = tokenizer2.encode(prompt, return_tensors="pt", max_length=max_length, truncation=True)
-        outputs = model2(inputs).logits
-        generated_tokens = outputs.argmax(dim=-1)
-        return tokenizer2.decode(generated_tokens[0], skip_special_tokens=True).replace(prompt, "").strip()
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
+def generate_initial_argument(query):
+    prompt = f"Topic: {query}\n\nProvide a logical explanation supporting this topic:\n"
+    return generate_argument(prompt, max_length=150)
+
+def generate_counter_argument(query, initial_argument):
+    prompt = f"Topic: {query}\n\nInitial argument: {initial_argument}\n\nProvide a well-reasoned counter-argument:\n"
+    return generate_argument(prompt, max_length=200, temperature=0.8)
 
 def debate(query):
-    initial_argument = generate_gpt2_argument(query)
-    counter_argument = generate_bert_argument(query, initial_argument)
+    initial_argument = generate_initial_argument(query)
+    counter_argument = generate_counter_argument(query, initial_argument)
     return initial_argument, counter_argument
 
 # Define the Gradio interface
@@ -46,12 +39,12 @@ iface = gr.Interface(
     inputs=gr.Textbox(lines=2, placeholder="Enter your question or topic for debate here..."),
     outputs=[
         gr.Textbox(label="Initial Argument (GPT-2)"),
-        gr.Textbox(label="Counter-Argument (BERT)")
+        gr.Textbox(label="Counter-Argument (GPT-2)")
     ],
-    title="Two-Model Debate System",
-    description="Enter a question or topic. GPT-2 will provide an initial argument, and BERT will generate a counter-argument.",
+    title="Two-Perspective Debate System",
+    description="Enter a question or topic. GPT-2 will provide an initial argument and a counter-argument.",
     examples=[
-        ["What are the long-term implications of artificial intelligence on employment?"],
+        ["Is it good for kids to go to schools, or are they wasting their time?"],
         ["Should governments prioritize space exploration or addressing climate change?"],
         ["Is genetic engineering in humans ethical for disease prevention?"]
     ]