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app.py

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+import streamlit as st
+from llama_cpp import Llama
+
+# Load the GGUF model from Hugging Face
+@st.cache_resource
+def load_model():
+    llm = Llama.from_pretrained(
+        repo_id="kshitij230/Eunoia",
+        filename="unsloth.Q8_0.gguf",
+        n_ctx=2048,
+        chat_format="llama-2"  # Change this if your model uses a different format
+    )
+    return llm
+
+llm = load_model()
+
+# Streamlit page config
+st.set_page_config(page_title="Eunoia 💜", layout="centered")
+st.title("🫂 Eunoia - Your Emotional Support Bot")
+st.markdown("Speak your heart. I'm here to listen and help 💖")
+
+# Chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Show history
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+# Input box
+user_input = st.chat_input("What's on your mind?")
+
+if user_input:
+    # Display user message
+    st.chat_message("user").markdown(user_input)
+    st.session_state.messages.append({"role": "user", "content": user_input})
+
+    # Build conversation prompt (simple format)
+    prompt = ""
+    for msg in st.session_state.messages:
+        role = "User" if msg["role"] == "user" else "Bot"
+        prompt += f"{role}: {msg['content']}\n"
+    prompt += "Bot:"
+
+    # Generate model response
+    with st.spinner("Eunoia is listening..."):
+        response = llm(prompt, max_tokens=200, stop=["User:", "Bot:"])
+        reply = response["choices"][0]["text"].strip()
+
+    # Display bot reply
+    st.chat_message("assistant").markdown(reply)
+    st.session_state.messages.append({"role": "assistant", "content": reply})

requirements.txt

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+transformers
+torch
+streamlit
+llama-cpp-python
+huggingface_hub