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Update app.py
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app.py
CHANGED
@@ -3,28 +3,40 @@ import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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import os
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from threading import Thread
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import time
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#
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@st.cache_resource()
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def load_model():
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model
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model, tok = load_model()
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terminators = [tok.eos_token_id]
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Initialize session state if not set
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if "chat_history" not in st.session_state:
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@@ -32,13 +44,19 @@ if "chat_history" not in st.session_state:
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# Chat function
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def chat(message, temperature, do_sample, max_tokens):
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chat_log.append({"role": "user", "content": message})
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messages = tok.apply_chat_template(chat_log, tokenize=False, add_generation_prompt=True)
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model_inputs = tok([messages], return_tensors="pt").to(device)
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streamer = TextIteratorStreamer(tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = {
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"inputs": model_inputs["input_ids"],
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"streamer": streamer,
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@@ -50,46 +68,61 @@ def chat(message, temperature, do_sample, max_tokens):
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if temperature == 0:
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generate_kwargs["do_sample"] = False
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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for new_text in streamer:
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yield
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st.session_state.chat_history.append({"role": "assistant", "content": partial_text})
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#
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st.title("π¬ Chat With Phi-4-Hindi")
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st.markdown("Chat with [large-traversaal/Phi-4-Hindi](https://huggingface.co/large-traversaal/Phi-4-Hindi)")
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# Chat
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temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.3, 0.1)
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do_sample = st.sidebar.checkbox("Use Sampling", value=True)
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max_tokens = st.sidebar.slider("Max Tokens", 128, 4096, 512, 1)
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text_color = st.sidebar.selectbox("Text Color", ["Red", "Black", "Blue", "Green", "Purple"], index=0)
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dark_mode = st.sidebar.checkbox("π Dark Mode", value=False)
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def get_html_text(text, color):
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return f'<p style="color: {color.lower()}; font-size: 16px;">{text}</p>'
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for msg in st.session_state.chat_history:
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if msg["role"] == "user"
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else:
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st.markdown(get_html_text("π€ " + msg["content"], text_color), unsafe_allow_html=True)
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user_input = st.text_input("Type your message:", "")
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if st.button("Send"):
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if user_input.strip():
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st.session_state.chat_history.append({"role": "user", "content": user_input})
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st.experimental_rerun()
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if st.button("π§Ή Clear Chat"):
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st.
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st.experimental_rerun()
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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import os
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from threading import Thread
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# Define model path for caching (Avoids reloading every app restart)
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MODEL_PATH = "/mnt/data/Phi-4-Hindi"
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TOKEN = os.environ.get("HF_TOKEN")
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MODEL_NAME = "large-traversaal/Phi-4-Hindi"
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# Load Model & Tokenizer Once
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@st.cache_resource()
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def load_model():
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with st.spinner("Loading model... Please wait β³"):
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if not os.path.exists(MODEL_PATH):
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME, token=TOKEN, trust_remote_code=True, torch_dtype=torch.bfloat16
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)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=TOKEN)
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model.save_pretrained(MODEL_PATH)
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tokenizer.save_pretrained(MODEL_PATH)
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else:
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model = AutoModelForCausalLM.from_pretrained(MODEL_PATH)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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return model, tokenizer
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# Load and move model to appropriate device
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model, tok = load_model()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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try:
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model = model.to(device)
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except torch.cuda.OutOfMemoryError:
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st.error("β οΈ CUDA Out of Memory! Running on CPU instead.")
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device = torch.device("cpu")
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model = model.to(device)
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terminators = [tok.eos_token_id]
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# Initialize session state if not set
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if "chat_history" not in st.session_state:
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# Chat function
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def chat(message, temperature, do_sample, max_tokens):
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"""Processes chat input and generates a response using the model."""
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# Append new message to history
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st.session_state.chat_history.append({"role": "user", "content": message})
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# Convert chat history into model-friendly format
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messages = tok.apply_chat_template(st.session_state.chat_history, tokenize=False, add_generation_prompt=True)
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model_inputs = tok([messages], return_tensors="pt").to(device)
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# Initialize streamer for token-wise response
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streamer = TextIteratorStreamer(tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
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# Define generation parameters
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generate_kwargs = {
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"inputs": model_inputs["input_ids"],
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"streamer": streamer,
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if temperature == 0:
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generate_kwargs["do_sample"] = False
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# Generate response asynchronously
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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# Collect response as it streams
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response_text = ""
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for new_text in streamer:
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response_text += new_text
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yield response_text
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# Save the assistant's response to session history
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st.session_state.chat_history.append({"role": "assistant", "content": response_text})
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# UI Setup
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st.title("π¬ Chat With Phi-4-Hindi")
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st.success("β
Model is READY to chat!")
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st.markdown("Chat with [large-traversaal/Phi-4-Hindi](https://huggingface.co/large-traversaal/Phi-4-Hindi)")
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# Sidebar Chat Settings
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temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.3, 0.1)
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do_sample = st.sidebar.checkbox("Use Sampling", value=True)
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max_tokens = st.sidebar.slider("Max Tokens", 128, 4096, 512, 1)
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text_color = st.sidebar.selectbox("Text Color", ["Red", "Black", "Blue", "Green", "Purple"], index=0)
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dark_mode = st.sidebar.checkbox("π Dark Mode", value=False)
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# Function to format chat messages
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def get_html_text(text, color):
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return f'<p style="color: {color.lower()}; font-size: 16px;">{text}</p>'
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# Display chat history
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for msg in st.session_state.chat_history:
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role = "π€" if msg["role"] == "user" else "π€"
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st.markdown(get_html_text(f"**{role}:** {msg['content']}", text_color if role == "π€" else "black"), unsafe_allow_html=True)
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# User Input Handling
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user_input = st.text_input("Type your message:", "")
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if st.button("Send"):
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if user_input.strip():
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st.session_state.chat_history.append({"role": "user", "content": user_input})
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# Display chatbot response
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with st.spinner("Generating response... π€π"):
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response_generator = chat(user_input, temperature, do_sample, max_tokens)
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final_response = ""
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for output in response_generator:
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final_response = output # Store latest output
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#st.success("β
Response generated!")
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# Add generated response to session state
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st.experimental_rerun()
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if st.button("π§Ή Clear Chat"):
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with st.spinner("Clearing chat history..."):
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st.session_state.chat_history = []
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st.success("β
Chat history cleared!")
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st.experimental_rerun()
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