Create app.py

app.py

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+# Import the necessary modules
+import google.generativeai as genai
+import streamlit as st
+
+# Configure the google-generativeai library by providing your API key
+genai.configure(api_key="AIzaSyC70u1sN87IkoxOoIj4XCAPw97ae2LZwNM")
+
+# Set up the model
+generation_config = {
+  "temperature": 0.9,
+  "top_p": 1,
+  "top_k": 1,
+  "max_output_tokens": 2048,
+}
+
+safety_settings = [
+  {
+    "category": "HARM_CATEGORY_HARASSMENT",
+    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+  },
+  {
+    "category": "HARM_CATEGORY_HATE_SPEECH",
+    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+  },
+  {
+    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+  },
+  {
+    "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+  }
+]
+
+model = genai.GenerativeModel(model_name="gemini-pro",
+                              generation_config=generation_config,
+                              safety_settings=safety_settings)
+
+# Create a streamlit interface with a title, a description, an input box, and an output box
+st.title("Gemini API") # the title of the interface
+st.markdown("A simple interface for Gemini API that explains code snippets in natural language.") # the description of the interface
+user_input = st.text_area("User Input", height=200) # the input box for the user input
+if st.button("Generate"): # the button to trigger the generation
+  # Start a chat session with the model
+  convo = model.start_chat(history=[])
+  # Send the user input to the model
+  convo.send_message(user_input)
+  # Get the response from the model
+  response = convo.last.text
+  # Display the response in the output box
+  st.text_area("Response", response, height=200) # the output box for the response