Create app.py

app.py

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+import transformers
+from transformers import AutoTokenizer
+import torch
+from transformers import pipeline
+
+model = "meta-llama/Llama-2-7b-chat-hf"
+tokenizer = AutoTokenizer.from_pretrained(model, use_auth_token=True)
+
+SYSTEM_PROMPT = """<s>[INST] <<SYS>>
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+<</SYS>>
+
+"""
+
+# Formatting function for message and history
+def message_format(message: str, history: list, memory_limit: int = 5) -> str:
+    
+    # always keep len(history) <= memory_limit
+    if len(history) > memory_limit:
+        history = history[-memory_limit:]
+
+    if len(history) == 0:
+        return SYSTEM_PROMPT + f"{message} [/INST]"
+
+    formatted_message = SYSTEM_PROMPT + f"{history[0][0]} [/INST] {history[0][1]} </s>"
+
+    # Handle conversation history
+    for user_msg, model_answer in history[1:]:
+        formatted_message += f"<s>[INST] {user_msg} [/INST] {model_answer} </s>"
+
+    # Handle the current message
+    formatted_message += f"<s>[INST] {message} [/INST]"
+
+    return formatted_message
+
+# Generate a response from the Llama model
+def llama_response(message: str, history: list) -> str:
+  
+    query = message_format(message, history)
+    response = ""
+
+    sequences = pipeline(
+        query,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+        max_length=1050,
+    )
+
+    generated_text = sequences[0]['generated_text']
+    response = generated_text[len(query):]  # Remove the prompt from the output
+
+    print("Chatbot:", response.strip())
+    return response.strip()
+
+import gradio as gr
+
+gr.ChatInterface(llama_response).launch()