Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer from Hugging Face
+model_name = "unsloth/Llama-3.2-1b-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+# Chat history
+chat_history = []
+
+def generate_response(message, history):
+    # Combine message with history
+    prompt = ""
+    for user, bot in history:
+        prompt += f"<|user|>{user}<|end|><|assistant|>{bot}<|end|>"
+    prompt += f"<|user|>{message}<|end|><|assistant|>"
+
+    # Tokenize and generate
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract only the assistant's latest message
+    response = result.split("<|assistant|>")[-1].strip()
+    history.append((message, response))
+    return response, history
+
+# Gradio UI
+chatbot = gr.ChatInterface(fn=generate_response, 
+                           title="Llama 3.2 Chatbot",
+                           chatbot=gr.Chatbot(),
+                           textbox=gr.Textbox(placeholder="Ask me anything...", lines=2),
+                           clear_btn="Clear")
+
+chatbot.launch()