Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "sweatSmile/marx-opt350m-finetuned-v1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32,
+    device_map="auto"
+)
+
+def generate_response(prompt, max_tokens=300, temperature=0.7):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(label="Ask a question about Marxist theory, politics, or history", lines=5),
+        gr.Slider(50, 512, step=10, value=300, label="Max Tokens"),
+        gr.Slider(0.1, 1.0, step=0.1, value=0.7, label="Temperature")
+    ],
+    outputs=gr.Textbox(label="MarxBot's Response"),
+    title="Dialectic Dialogues: Ask MarxGPT",
+    description="A dialogue-optimized language model trained on Marxist political and philosophical content."
+).launch()