add

app.py

@@ -0,0 +1,37 @@
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("your-hf-username/your-model-name")
+model = AutoModelForCausalLM.from_pretrained("your-hf-username/your-model-name")
+
+# Define function to generate responses
+def generate_response(persona, prompt, max_length=100):
+    full_prompt = f"You are {persona}. {prompt}"
+    inputs = tokenizer(full_prompt, return_tensors="pt", padding=True, truncation=True).to("cuda")
+    outputs = model.generate(inputs.input_ids, max_length=max_length, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Gradio interface
+examples = [
+    ["a grizzled pirate afraid of sharks", "What do you think of the open sea?"],
+    ["a curious scientist exploring Mars", "What do you think about the red planet?"],
+    ["a medieval knight on a quest for treasure", "How do you feel about dragons?"]
+]
+
+interface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(label="Describe your persona", placeholder="e.g., a grizzled pirate afraid of sharks"),
+        gr.Textbox(label="Enter your prompt", placeholder="e.g., What do you think of the open sea?"),
+        gr.Slider(10, 200, value=100, step=10, label="Response Length")
+    ],
+    outputs="text",
+    examples=examples,
+    title="Custom Persona Chatbot",
+    description="Create a persona and interact with the chatbot. Describe the persona and ask any question!"
+)
+
+interface.launch()

requirements.txt

@@ -0,0 +1,8 @@
+
+gradio
+transformers
+torch
+bitsandbytes
+accelerate 
+peft
+langdetect