Create app.py

app.py

@@ -0,0 +1,61 @@
+from transformers import AutoTokenizer
+import transformers
+import torch
+import gradio as gr
+
+
+model = "Harshithacj123/CCU_Llama_7b_chat_test" # meta-llama/Llama-2-7b-chat-hf
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+
+from transformers import pipeline
+
+llama_pipeline = pipeline(
+    "text-generation",  # LLM task
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+SYSTEM_PROMPT = """<s>[INST] <<SYS>>
+You are a helpful bot. Your answers are clear and concise.
+<</SYS>>
+
+"""
+
+# Formatting function for message and history
+def format_message(message: str) -> str:
+    
+    formatted_message = f"<s>[INST] {message} [/INST]"
+
+    return formatted_message
+
+# Generate a response from the Llama model
+def get_llama_response(message: str) -> str:
+    """
+    Generates a conversational response from the Llama model.
+    Parameters:
+        message (str): User's input message.
+        history (list): Past conversation history.
+    Returns:
+        str: Generated response from the Llama model.
+    """
+    query = format_message(message)
+    response = ""
+
+    sequences = llama_pipeline(
+        query,
+        do_sample=True,
+        top_k=10,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+        max_length=256,
+    )
+
+    generated_text = sequences[0]['generated_text']
+    response = generated_text[len(query):]  # Remove the prompt from the output
+
+    return response.strip()
+
+
+gr.ChatInterface(get_llama_response).launch()