Update app.py

app.py

@@ -1,18 +1,106 @@
-# Use a pipeline as a high-level helper
-from transformers import pipeline
+'''🔧 Prerequisites
+
+Install the necessary packages:
+
+pip install gradio transformers
+
+📱 Gradio Chatbot App Code
+'''
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# List of available premium models
 premium_models = [
     "HuggingFaceH4/zephyr-7b-beta",
-     "K00B404/BagOClownCoders-slerp-7B",
+    "K00B404/BagOClownCoders-slerp-7B",
     "Qwen/Qwen2.5-Omni-7B",
     "Qwen/Qwen2.5-VL-7B-Instruct",
     "deepseek-ai/Janus-Pro-7B",
     "meta-llama/Llama-2-7b-hf",
     "Alibaba-NLP/gte-Qwen2-7B-instruct",
-   
 ]
-messages = [
-    {"role": "system_prompt", "content": "You are a ChatBuddy and chat with the user in a Human way"},
-    {"role": "user", "content": "Who are you?"}
-]
-pipe = pipeline("text-generation", model=premium_models[0])
-pipe(messages)
+
+# Dictionary to cache loaded pipelines
+pipeline_cache = {}
+
+# Initial system prompt
+default_system_prompt = "You are a ChatBuddy and chat with the user in a Human way."
+
+def load_pipeline(model_name):
+    if model_name not in pipeline_cache:
+        print(f"Loading model: {model_name}")
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
+        pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
+        pipeline_cache[model_name] = pipe
+    return pipeline_cache[model_name]
+
+def chatbot(user_input, history, model_choice):
+    pipe = load_pipeline(model_choice)
+
+    # Prepare the chat messages
+    messages = [{"role": "system", "content": default_system_prompt}]
+    for pair in history:
+        messages.append({"role": "user", "content": pair[0]})
+        messages.append({"role": "assistant", "content": pair[1]})
+    messages.append({"role": "user", "content": user_input})
+
+    # Flatten into a prompt string
+    prompt = ""
+    for msg in messages:
+        if msg["role"] == "system":
+            prompt += f"<|system|> {msg['content']}\n"
+        elif msg["role"] == "user":
+            prompt += f"<|user|> {msg['content']}\n"
+        elif msg["role"] == "assistant":
+            prompt += f"<|assistant|> {msg['content']}\n"
+    
+    # Generate a response
+    response = pipe(prompt, max_new_tokens=200, do_sample=True, top_p=0.95, temperature=0.7)[0]['generated_text']
+    
+    # Extract only the last assistant response
+    split_res = response.split("<|assistant|>")
+    final_response = split_res[-1].strip() if len(split_res) > 1 else response
+
+    history.append((user_input, final_response))
+    return "", history
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 ChatBuddy - Advanced Chatbot with Selectable LLMs")
+
+    with gr.Row():
+        model_choice = gr.Dropdown(label="Select Model", choices=premium_models, value=premium_models[0])
+    
+    chatbot_ui = gr.Chatbot()
+    user_input = gr.Textbox(show_label=False, placeholder="Type your message and press Enter")
+    clear_btn = gr.Button("Clear")
+
+    state = gr.State([])
+
+    user_input.submit(chatbot, [user_input, state, model_choice], [user_input, chatbot_ui])
+    clear_btn.click(lambda: ([], ""), None, [chatbot_ui, state])
+
+demo.launch()
+'''
+✅ Features:
+
+    Model selection from dropdown
+
+    Maintains chat history
+
+    Respects a system prompt
+
+    Uses text-generation pipeline
+
+🧠 Optional Upgrades:
+
+    Replace text-generation with chat-completion if models support it (like OpenChat, Mistral-instruct, etc.)
+
+    Add streaming or token-by-token response if supported
+
+    Save/load chat history
+
+    Add support for vision models (Qwen2.5-VL-7B-Instruct) using a different UI tab
+
+'''