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test4.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the pre-trained Llama model and tokenizer
+model_name = "meta-llama/Llama-2-13b-chat-hf"
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
+
+# Define a system prompt to set the context and behavior
+system_prompt = "You are chatting with a friendly AI. Ask me anything!"
+
+# Function to generate a response
+def chat(input_text):
+    # Combine the system prompt and user input
+    full_prompt = f"{system_prompt}\n\n{input_text}"
+
+    # Encode the combined prompt and generate a response
+    input_ids = tokenizer.encode(full_prompt, return_tensors="pt")
+    with torch.no_grad():
+        output = model.generate(input_ids, max_length=50, num_return_sequences=1)
+    
+    # Decode and return the AI's response
+    ai_response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return ai_response
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=chat,
+    inputs="text",
+    outputs="text",
+    title="Llama Chatbot",
+    description="Chat with a friendly AI chatbot powered by the Llama model.",
+    live=True
+)
+
+# Launch the Gradio interface
+iface.launch()