Update app.py

app.py

@@ -1,12 +1,50 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-def greet(name):
-    return "Hello " + name + "!"
+# Load the LLaMA-2 model and tokenizer from Hugging Face
+model_name = "meta-llama/Llama-2-7b-hf"  # Change to the desired LLaMA model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
+model = model.to("cuda" if torch.cuda.is_available() else "cpu")
 
-demo = gr.Interface(lambda x: x, "image", "image")
+# Function to generate responses
+def generate_response(user_input, chat_history):
+    # Add the user's input to the conversation history
+    chat_history.append({"role": "user", "content": user_input})
 
-demo.launch(max_file_size="5mb")
-# or
-demo.launch(max_file_size=5 * gr.FileSize.MB)
+    # Prepare input for the model
+    conversation = ""
+    for turn in chat_history:
+        conversation += f"{turn['role']}: {turn['content']}\n"
+    inputs = tokenizer(conversation, return_tensors="pt").to(model.device)
+
+    # Generate model response
+    outputs = model.generate(inputs.input_ids, max_length=500, do_sample=True, temperature=0.7)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Add the model's response to the chat history
+    chat_history.append({"role": "assistant", "content": response})
     
-demo.launch(share=True)  # Share your demo with just 1 extra parameter 🚀
+    # Only return the model's response for display
+    return response, chat_history
+
+# Initialize the chat history
+chat_history = []
+
+# Define Gradio interface
+with gr.Blocks() as chat_interface:
+    gr.Markdown("## LLaMA-2 Chatbot")
+    chat_input = gr.Textbox(label="Your Message")
+    chat_output = gr.Chatbot()
+
+    # Update chat on button click
+    def handle_input(user_input):
+        response, chat_history = generate_response(user_input, chat_history)
+        chat_output.update(chat_history)
+        return "", chat_history  # Clear input box and update chat history
+
+    chat_input.submit(handle_input, inputs=chat_input, outputs=[chat_input, chat_output])
+
+# Launch Gradio app
+chat_interface.launch()