Added Gemma model, model selection, inference, sliders and UI

.gitignore

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+__pycache__/
+*.pyc
+.env
+assets/*.gif

README.md

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+# 💎 Gemma 💎 HF Spaces Demo
+
+An interactive [Streamlit](https://streamlit.io) app to test [Gemma](https://huggingface.co/google/gemma-2b) models directly in your browser.
+
+## Features 🚀
+
+- Chat with the Gemma model (default: `google/gemma-2b`)
+- Fast deploy to Hugging Face Spaces
+- Easy to customize & extend
+
+## Setup 📦
+
+```bash
+pip install -r requirements.txt
+streamlit run app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import base64
+
+st.set_page_config(page_title="Gemma Demo", layout="wide")
+# Model selection (STUBBED behavior)
+model_option = st.selectbox(
+    "Choose a Gemma to reveal hidden truths:",
+    ["gemma-2b-it (Instruct)", "gemma-2b", "gemma-7b", "gemma-7b-it"],
+    index=0,
+    help="Stubbed selection – only gemma-2b-it will load for now."
+)
+st.markdown("<h1 style='text-align: center;'>Portal to Gemma</h1>", unsafe_allow_html=True)
+
+# Load both GIFs in base64 format
+def load_gif_base64(path):
+    with open(path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+still_gem_b64 = load_gif_base64("assets/stillGem.gif")
+rotating_gem_b64 = load_gif_base64("assets/rotatingGem.gif")
+
+# Placeholder for GIF HTML
+gif_html = st.empty()
+caption = st.empty()
+
+# Initially show still gem
+gif_html.markdown(
+    f"<div style='text-align:center;'><img src='data:image/gif;base64,{still_gem_b64}' width='300'></div>",
+    unsafe_allow_html=True,
+)
+
+@st.cache_resource
+def load_model():
+    model_id = "google/gemma-2b-it"
+    tokenizer = AutoTokenizer.from_pretrained(model_id, token=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        device_map=None,
+        torch_dtype=torch.float32
+    )
+    model.to("cpu")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+prompt = st.text_area("Enter your prompt:", "What is Gemma?")
+# # Example prompt selector
+# examples = {
+#     "🧠 Summary": "Summarize the history of AI in 5 bullet points.",
+#     "💻 Code": "Write a Python function to sort a list using bubble sort.",
+#     "📜 Poem": "Write a haiku about large language models.",
+#     "🤖 Explain": "Explain what a transformer is in simple terms.",
+#     "🔍 Fact": "Who won the FIFA World Cup in 2022?"
+# }
+
+# selected_example = st.selectbox("Choose a Gemma to consult:", list(examples.keys()) + ["✍️ Custom input"])
+# Add before generation
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    temperature = st.slider("Temperature", 0.1, 1.5, 1.0)
+
+with col2:
+    max_tokens = st.slider("Max tokens", 50, 500, 100)
+
+with col3:
+    top_p = st.slider("Top-p (nucleus sampling)", 0.1, 1.0, 0.95)
+# if selected_example != "✍️ Custom input":
+#     prompt = examples[selected_example]
+# else:
+#     prompt = st.text_area("Enter your prompt:")
+
+if st.button("Generate"):
+    # Swap to rotating GIF
+    gif_html.markdown(
+        f"<div style='text-align:center;'><img src='data:image/gif;base64,{rotating_gem_b64}' width='300'></div>",
+        unsafe_allow_html=True,
+    )
+    caption.markdown("<p style='text-align: center;'>Gemma is thinking... 🌀</p>", unsafe_allow_html=True)
+
+
+    # Generate text
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p)
+
+    # Back to still
+    gif_html.markdown(
+        f"<div style='text-align:center;'><img src='data:image/gif;base64,{still_gem_b64}' width='300'></div>",
+        unsafe_allow_html=True,
+    )
+    caption.empty()
+
+
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    st.markdown("### ✨ Output:")
+    st.write(result)

requirements.txt

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+streamlit
+transformers
+torch
+accelerate