Update app.py

app.py

@@ -0,0 +1,54 @@
+# app.py
+import streamlit as st
+import os
+from dotenv import load_dotenv
+from langchain_huggingface import HuggingFaceEndpoint
+
+# Load HuggingFace API token
+load_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Initialize the HuggingFace LLM
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    huggingfacehub_api_token=HF_TOKEN.strip(),
+    temperature=0.7,
+    task="text-generation",
+    max_new_tokens=200
+)
+
+# Streamlit UI setup
+st.set_page_config(page_title="🧠 HuggingFace Chatbot", page_icon="🤖")
+st.title("🤖 HuggingFace Chatbot")
+st.caption("Built with Streamlit + LangChain (No schema!)")
+
+# Initialize session state for chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {"role": "assistant", "content": "Hi there! Ask me anything."}
+    ]
+
+# Display chat history
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+# Handle user input
+if prompt := st.chat_input("Type your message here..."):
+    # Add user message
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Prepare context as a plain string (no schema)
+    context = ""
+    for msg in st.session_state.messages:
+        role = "User" if msg["role"] == "user" else "Assistant"
+        context += f"{role}: {msg['content']}\n"
+    context += "Assistant:"
+
+    # Generate response from LLM
+    with st.chat_message("assistant"):
+        response = llm.invoke(context)
+        st.markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response})