Create app.py

app.py

@@ -0,0 +1,95 @@
+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import os
+from threading import Thread
+import time
+
+# Load Model and Tokenizer
+token = os.environ.get("HF_TOKEN")
+model_name = "large-traversaal/Phi-4-Hindi"
+
+@st.cache_resource()
+def load_model():
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        token=token,
+        trust_remote_code=True,
+        torch_dtype=torch.bfloat16
+    )
+    tok = AutoTokenizer.from_pretrained(model_name, token=token)
+    return model, tok
+
+model, tok = load_model()
+terminators = [tok.eos_token_id]
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# Initialize session state if not set
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Chat function
+def chat(message, temperature, do_sample, max_tokens):
+    chat_log = st.session_state.chat_history.copy()
+    chat_log.append({"role": "user", "content": message})
+    messages = tok.apply_chat_template(chat_log, tokenize=False, add_generation_prompt=True)
+    
+    model_inputs = tok([messages], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+    
+    generate_kwargs = {
+        "inputs": model_inputs["input_ids"],
+        "streamer": streamer,
+        "max_new_tokens": max_tokens,
+        "do_sample": do_sample,
+        "temperature": temperature,
+        "eos_token_id": terminators,
+    }
+    
+    if temperature == 0:
+        generate_kwargs["do_sample"] = False
+    
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    
+    partial_text = ""
+    for new_text in streamer:
+        partial_text += new_text
+        yield partial_text
+    
+    st.session_state.chat_history.append({"role": "assistant", "content": partial_text})
+
+# Streamlit UI
+st.title("💬 Chat With Phi-4-Hindi")
+st.markdown("Chat with [large-traversaal/Phi-4-Hindi](https://huggingface.co/large-traversaal/Phi-4-Hindi)")
+
+# Chat input
+temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.3, 0.1)
+do_sample = st.sidebar.checkbox("Use Sampling", value=True)
+max_tokens = st.sidebar.slider("Max Tokens", 128, 4096, 512, 1)
+text_color = st.sidebar.selectbox("Text Color", ["Red", "Black", "Blue", "Green", "Purple"], index=0)
+dark_mode = st.sidebar.checkbox("🌙 Dark Mode", value=False)
+
+def get_html_text(text, color):
+    return f'<p style="color: {color.lower()}; font-size: 16px;">{text}</p>'
+
+for msg in st.session_state.chat_history:
+    if msg["role"] == "user":
+        st.markdown(get_html_text("👤 " + msg["content"], "black"), unsafe_allow_html=True)
+    else:
+        st.markdown(get_html_text("🤖 " + msg["content"], text_color), unsafe_allow_html=True)
+
+user_input = st.text_input("Type your message:", "")
+if st.button("Send"):
+    if user_input.strip():
+        st.session_state.chat_history.append({"role": "user", "content": user_input})
+        with st.spinner("Generating response..."):
+            for output in chat(user_input, temperature, do_sample, max_tokens):
+                pass
+        st.experimental_rerun()
+
+if st.button("🧹 Clear Chat"):
+    st.session_state.chat_history = []
+    st.experimental_rerun()