Create app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
+
+def generate_text(prompt, max_length=150):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1, do_sample=True)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def generate_argument(topic, stance):
+    prompt = f"Generate a compelling argument for the following debate topic.\nTopic: {topic}\nStance: {stance}\nArgument:"
+    response = generate_text(prompt, max_length=200)
+    argument = response.split("Argument:")[1].strip()
+    return argument
+
+def generate_counterargument(topic, original_argument):
+    prompt = f"Generate a strong counterargument for the following debate topic and argument.\nTopic: {topic}\nOriginal Argument: {original_argument}\nCounterargument:"
+    response = generate_text(prompt, max_length=200)
+    counterargument = response.split("Counterargument:")[1].strip()
+    return counterargument
+
+def debate_assistant(topic, stance):
+    argument = generate_argument(topic, stance)
+    counterargument = generate_counterargument(topic, argument)
+    return f"Argument: {argument}\n\nCounterargument: {counterargument}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=debate_assistant,
+    inputs=[
+        gr.Textbox(label="Debate Topic"),
+        gr.Radio(["For", "Against"], label="Stance")
+    ],
+    outputs=gr.Textbox(label="Generated Debate Arguments"),
+    title="AI-powered Debate Assistant (Meta-Llama 3.1)",
+    description="Enter a debate topic and choose a stance to generate arguments and counterarguments using Meta-Llama 3.1."
+)
+
+# Launch the interface
+iface.launch()