Update app.py

app.py

@@ -1,3 +1,43 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-gr.load("models/meta-llama/Meta-Llama-3.1-70B-Instruct").launch()
+# Load model and tokenizer from Hugging Face Model Hub
+model_name = "meta-llama/Meta-Llama-3.1-70B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Define system instruction
+system_instruction = "You are a helpful assistant. Provide detailed and accurate responses to the user's queries."
+
+# Define the chat function
+def chat_function(prompt):
+    # Create the full input prompt including the system instruction
+    full_prompt = f"{system_instruction}\nUser: {prompt}\nAssistant:"
+    
+    # Tokenize the full prompt
+    inputs = tokenizer(full_prompt, return_tensors="pt")
+    
+    # Generate model response
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=150, num_return_sequences=1)
+    
+    # Decode and return response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+    
+    # Extract only the assistant's response
+    response = response.split("Assistant:")[-1].strip()
+    return response
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=chat_function,
+    inputs="text",
+    outputs="text",
+    title="Meta-Llama Chatbot",
+    description="A chatbot powered by the Meta-Llama-3.1-70B-Instruct model."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()