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README.md

@@ -9,5 +9,18 @@ app_file: app.py
 pinned: false
 license: mit
 ---
+# Chatbot with Streamlit and LangChain
+
+This project implements a chatbot using Streamlit, LangChain, and the Mistral Chat API. The chatbot takes user input and responds with answers generated using the language model.
+
+## Features
+
+- User-friendly interface built with Streamlit.
+- Integration with LangChain for handling prompts.
+- API integration with Mistral for generating responses.
+- Environment variable management with `python-dotenv`.
+
+## Contributing
+**Contributions are welcome! Please open an issue or submit a pull request for any changes or improvements.**
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,49 @@
+# make sure to install `langchain` and `langchain-mistralai` in your Python environment
+
+import os
+from langchain_mistralai import ChatMistralAI
+import streamlit as st
+from dotenv import load_dotenv
+from langchain_core.output_parsers import StrOutputParser
+
+# Initialize session state
+if 'result' not in st.session_state:
+    st.session_state.result = None
+
+# Langchain settings
+load_dotenv(".env")
+os.environ["LANGCHAIN_PROJECT"] = "mistral_app"
+os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
+# Initialize the LLM
+@st.cache_resource
+def initialize_llm():
+    api_key = os.getenv("API_KEY")
+    mistral_model = "open-codestral-mamba"
+    return ChatMistralAI(
+        model=mistral_model, 
+        temperature=0, 
+        api_key=api_key,
+        token_limit=2000,
+        random_seed=0
+    )
+
+llm = initialize_llm()
+parser = StrOutputParser()
+
+#streamlit framework
+st.title("Chatbot(MistralAI)")
+input_text = st.text_input("Feel free to ask me anything")
+
+# Only make API call when submit button is pressed
+if st.button("Submit") and input_text:
+    with st.spinner("Thinking..."):
+        output = llm.invoke([("user", input_text)])
+        st.session_state.result = parser.invoke(output)
+
+# Display results
+if input_text:
+    st.write("You said: " + input_text)
+if st.session_state.result:
+    st.write(st.session_state.result)

requirements.txt

@@ -0,0 +1,5 @@
+langchain
+langchain-mistralai
+streamlit
+python-dotenv
+streamlit-feedback