test

app.py

@@ -1,14 +1,69 @@
 import gradio as gr
-import spaces
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import spaces
+
+# Dictionary to store loaded models and tokenizers
+loaded_models = {}
+
+# List of available models (update with your preferred models)
+models = ["gpt2", "gpt2-medium", "gpt2-large", "EleutherAI/gpt-neo-1.3B"]
+
 
-zero = torch.Tensor([0]).cuda()
-print(zero.device) # <-- 'cpu' 🤔
+def load_model(model_name):
+    if model_name not in loaded_models:
+        print(f"Loading model: {model_name}")
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")
+        loaded_models[model_name] = (model, tokenizer)
+    return loaded_models[model_name]
 
 @spaces.GPU
-def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
+def get_model_response(model_name, message):
+    model, tokenizer = load_model(model_name)
+    inputs = tokenizer(message, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=100, num_return_sequences=1, temperature=0.7)
+
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+
+def chat(message, history, model1, model2):
+    response1 = get_model_response(model1, message)
+    response2 = get_model_response(model2, message)
+    return [(message, f"{model1}: {response1}\n\n{model2}: {response2}")]
+
+
+def vote(direction, history):
+    if history:
+        last_interaction = history[-1]
+        vote_text = f"\n\nUser voted: {'👍' if direction == 'up' else '👎'}"
+        updated_interaction = (last_interaction[0], last_interaction[1] + vote_text)
+        return history[:-1] + [updated_interaction]
+    return history
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Hugging Face Model Comparison Chat")
+
+    with gr.Row():
+        model1_dropdown = gr.Dropdown(choices=models, label="Model 1", value=models[0])
+        model2_dropdown = gr.Dropdown(choices=models, label="Model 2", value=models[1])
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Your message")
+    clear = gr.Button("Clear")
+
+    with gr.Row():
+        upvote = gr.Button("👍 Upvote")
+        downvote = gr.Button("👎 Downvote")
+
+    msg.submit(chat, [msg, chatbot, model1_dropdown, model2_dropdown], chatbot)
+    clear.click(lambda: None, None, chatbot, queue=False)
+    upvote.click(vote, ["up", chatbot], chatbot)
+    downvote.click(vote, ["down", chatbot], chatbot)
 
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()