Create app.py

app.py

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+import streamlit as st
+from groq import Client, ModelType
+
+# Replace with your Groq API key
+GROQ_API_KEY = "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"
+
+# Define a function to get chatbot response
+def get_response(prompt):
+  client = Client(api_key=GROQ_API_KEY)
+  model = client.model(ModelType.Claude_V1)  # Change model type if desired
+  response = model.generate(prompt)
+  return response.text
+
+# Streamlit app layout
+st.title("Interactive Chatbot")
+
+# Input field for user message
+user_input = st.text_input("You: ", key="user_input")
+
+# Display chat history (optional, can be implemented with a list)
+
+# Generate response if user enters something
+if user_input:
+  # Get response from Groq API
+  bot_response = get_response(user_input)
+  st.write("Bot: ", bot_response)
+
+# Deployment on Hugging Face (refer to their documentation)
+# This code snippet is not included as it involves specific steps on their platform.
+# You can find deployment instructions for Streamlit apps on Hugging Face Spaces here:
+# https://docs.huggingface.com/notebooks/streamlit/
+
+# Run the app
+if __name__ == "__main__":
+  st.sidebar.title("Settings")
+  # Add any additional settings options here (e.g., model selection)
+  st.sidebar.write("Groq API Key: (hidden for security)")
+  st.sidebar.success("Connected to Groq!")
+  st.balloons()  # Enable notification popups for user interaction
+  st.experimental_autogenerated_data_reports()  # Enable data reports
+  st.server.main()