Update app.py

app.py

@@ -1,12 +1,10 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the Meta Llma 3.1 Instruct model for the initial argument
-model1 = gr.load("models/microsoft/GRIN-MoE")
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Load a different model for the counter-argument
-# We'll use GRIN as an example, but you can replace this with another suitable model
-model2 = gr.load("models/microsoft/GRIN-MoE")
+model1 = AutoModelForCausalLM.from_pretrained("microsoft/GRIN-MoE")
+tokenizer1 = AutoTokenizer.from_pretrained("microsoft/GRIN-MoE")
 
 def generate_initial_argument(query):
     prompt = f"Provide a logical explanation for the following topic: {query}"
@@ -14,11 +12,20 @@ def generate_initial_argument(query):
     return response
 
 def generate_counter_argument(query, initial_argument):
-    prompt = f"Given the topic '{query}' and the argument '{initial_argument}', provide a well-reasoned counter-argument:"
-    response = model2(prompt, max_length=200, num_return_sequences=1, temperature=0.7)[0]['generated_text']
-    # Extract the counter-argument from the generated text
-    counter_argument = response.split(prompt)[-1].strip()
-    return counter_argument
+    try:
+        prompt = f"Given the topic '{query}' and the initial argument '{initial_argument}', provide a well-reasoned counter-argument:"
+        inputs = tokenizer2(prompt, return_tensors="pt")
+        outputs = model2.generate(
+            **inputs,
+            max_length=200,
+            num_return_sequences=1,
+            temperature=0.7,
+            do_sample=True
+        )
+        counter_argument = tokenizer2.decode(outputs[0], skip_special_tokens=True)
+        return counter_argument.replace(prompt, "").strip()
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
 def debate(query):
     initial_argument = generate_initial_argument(query)