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edm.py

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+import os
+from huggingface_hub import InferenceClient
+import streamlit as st
+
+# Access your Hugging Face API token from the environment variable
+api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+if api_token is None:
+    st.error("Hugging Face API token is not set.")
+else:
+    st.title("Tanya Gizi!")
+
+    # Initialize chat history if not already present
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat history
+    for message in st.session_state.messages:
+        st.chat_message(message['role']).markdown(message['content'])
+
+    # Input area for the user
+    prompt = st.chat_input('Masukan pertanyaanmu di sini!')
+
+    # Process user input
+    if prompt:
+        st.chat_message('user').markdown(prompt)
+        st.session_state.messages.append({'role': 'user', 'content': prompt})
+
+        # Generate a response using InferenceClient
+        client = InferenceClient(
+            model="mistralai/Mistral-Large-Instruct-2407",
+            token=api_token
+        )
+
+        # Generating response
+        response = client.chat_completion(
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=100,
+            stream=False  # Disable streaming as it's not supported
+        )
+
+        response_text = response['choices'][0]['message']['content']
+
+        # Display and store the assistant's response
+        st.chat_message('assistant').markdown(response_text)
+        st.session_state.messages.append({'role': 'assistant', 'content': response_text})

requirements.txt

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+beautifulsoup4==4.12.3
+filelock==3.15.4
+huggingface_hub==0.23.4
+kagglehub==0.2.8
+pytz==2023.3
+Requests==2.32.3
+streamlit==1.36.0
+urllib3==1.26.19