Used chatgpt code and reverted requirements.txt back

app.py

@@ -1,35 +1,23 @@
 import gradio as gr
-import transformers
-from transformers import AutoTokenizer
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-model = "Glasshes/debate_v3.1_model"
-tokenizer = AutoTokenizer.from_pretrained(model)
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model,
-    device_map="auto",
-)
+model_name = "Glasshes/debate_v3.1_model"
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
-def generate_text(text):
-    return "hello" + text
-    sequences = pipeline(
-        text,
-        do_sample=True,
-        top_k=10,
-        num_return_sequences=1,
-        eos_token_id=tokenizer.eos_token_id,
-        max_length=500,
-    )
-    response = ""
-    for seq in sequences:
-        print(f"Result: {seq['generated_text']}")
-        response += {seq['generated_text']}
-    return response
+def generate_text(prompt):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    output = model.generate(input_ids, max_length=100, num_return_sequences=1)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
 
 iface = gr.Interface(
     fn=generate_text,
-    inputs=gr.Textbox(),
+    inputs="text",
     outputs="text",
+    live=True,
+    title="Debate Model",
+    description="This model generates text based on the input prompt.",
 )
 
-iface.launch()
+iface.launch()

requirements.txt

@@ -1,8 +1,7 @@
-transformers==4.7.0
-torch==2.0
+transformers
+torch
 gradio
-huggingface-hub==0.16.4
-huggingface-hub==0.0.8
+huggingface-hub
 diffusers
 autotrain-advanced==0.6.32
 accelerate