Create HG_model+Dataset_template.txt

HG_model+Dataset_template.txt

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# --- Model and Tokenizer Setup ---
+model_id = "gpt2"  # Replace with your chosen model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# --- Text Generation Function ---
+def generate_text(prompt, max_tokens=50, system_message=""):
+    # Your model inference here...
+    encoded_input = tokenizer(prompt, return_tensors='pt')
+    max_length = len(encoded_input["input_ids"][0]) + max_tokens
+    output_sequences = model.generate(input_ids=encoded_input["input_ids"], max_length=max_length)
+    generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
+    
+    # Incorporating the system message
+    full_message = system_message + "\n\nGenerated Text:\n" + generated_text
+    return full_message
+
+# --- Gradio Interface Setup ---
+iface = gr.Interface(
+    fn=generate_text, 
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Enter Text Here..."), 
+        gr.inputs.Slider(minimum=1, maximum=100, default=50, label="Max Tokens"),
+        gr.inputs.Textbox(label="System Message", placeholder="Enter a system message here...")
+    ], 
+    outputs="text"
+)
+
+# --- Launch the Interface ---
+iface.launch()