Create app.py

app.py

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+import gradio as gr
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+# GGUF model filenames
+MODELS = {
+    "DeepSeek Chat (7B Chat)": "deepseek-llm-7b-chat-Q4_K_M.gguf",
+    "LLaMA 2 7B Chat": "llama-2-7b-chat.Q4_K_M.gguf",
+    "Smol 7B Chat (GGUF)": "smol-7b-Q4_K_M.gguf",
+    "DeepSeek Coder (6.7B Instruct)": "deepseek-coder-6.7b-instruct-Q4_K_M.gguf",
+    # Tiny test model
+    "TinyLlama 1.1B Test": "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf"
+}
+
+# Corresponding Hugging Face repo IDs
+REPO_IDS = {
+    "DeepSeek Chat (7B Chat)": "TheBloke/deepseek-llm-7B-chat-GGUF",
+    "LLaMA 2 7B Chat": "TheBloke/Llama-2-7B-Chat-GGUF",
+    "Smol 7B Chat (GGUF)": "TheBloke/smol-7B-GGUF",
+    "DeepSeek Coder (6.7B Instruct)": "second-state/Deepseek-Coder-6.7B-Instruct-GGUF",
+    # Tiny test model repo
+    "TinyLlama 1.1B Test": "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
+}
+
+# System prompts per model
+SYSTEM_PROMPTS = {
+    "DeepSeek Chat (7B Chat)": "You are a helpful assistant for friendly conversation:",
+    "LLaMA 2 7B Chat": "You are a helpful general AI assistant:",
+    "Smol 7B Chat (GGUF)": "You are a friendly general chat assistant:",
+    "DeepSeek Coder (6.7B Instruct)": "You are an instruction-following assistant:",
+    "TinyLlama 1.1B Test": "You are a lightweight test chat assistant:"
+}
+
+loaded_models = {}
+
+def load_model(choice):
+    if choice not in loaded_models:
+        # Download GGUF model file from Hugging Face Hub
+        model_path = hf_hub_download(repo_id=REPO_IDS[choice], filename=MODELS[choice])
+        loaded_models[choice] = Llama(model=model_path, n_ctx=2048)
+    return loaded_models[choice]
+
+def generate_chat(model_choice, history):
+    llm = load_model(model_choice)
+    user_msg = history[-1][0]
+    prompt = f"{SYSTEM_PROMPTS.get(model_choice, '')}\n{user_msg}"
+    out = llm(prompt, max_tokens=150)["choices"][0]["text"]
+    history[-1] = (user_msg, out)
+    return history
+
+# Quick preset helper
+def preset_text(text):
+    return text
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Playground.Deeemoi — Multi-Model Chat + Quick Prompts")
+
+    selector = gr.Dropdown(list(MODELS.keys()), label="Choose Model")
+
+    with gr.Row():
+        explain_btn = gr.Button("Explain")
+        summarize_btn = gr.Button("Summarize")
+        translate_btn = gr.Button("Translate to Spanish")
+        code_help_btn = gr.Button("Code Help")
+
+    chat_ui = gr.ChatInterface(
+        fn=lambda history, model_choice=selector: generate_chat(model_choice, history),
+        title="Chat with Quantized GGUF Models"
+    )
+
+    explain_btn.click(lambda: preset_text("Explain this clearly:"), None, chat_ui)
+    summarize_btn.click(lambda: preset_text("Summarize this text:"), None, chat_ui)
+    translate_btn.click(lambda: preset_text("Translate to Spanish:"), None, chat_ui)
+    code_help_btn.click(lambda: preset_text("Help with this code snippet:"), None, chat_ui)
+
+    selector.change(lambda _: [], None, chat_ui)
+
+demo.launch()