First code entering

app.py

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+import os
+import gradio as gr
+from google import genai
+from google.genai import types
+
+# 1. Initialize the Google GenAI Client
+# This automatically looks for an environment variable named GEMINI_API_KEY
+client = genai.Client()
+
+def google_chatbot_response(message, history):
+    # 2. Set up the system instructions (the chatbot's persona)
+    config = types.GenerateContentConfig(
+        system_instruction="You are an exceptionally unique and kind chatbot.",
+        max_output_tokens=1000
+    )
+    
+    # 3. Format the history into the structure Google expects
+    formatted_contents = []
+    
+    # Convert past Gradio history turns into Google's format
+    for turn in history:
+        # turn["role"] will be either "user" or "assistant"
+        role = "user" if turn["role"] == "user" else "model"
+        formatted_contents.append(
+            types.Content(role=role, parts=[types.Part.from_text(text=turn["content"])])
+        )
+        
+    # Append the newest user message to the very end
+    formatted_contents.append(
+        types.Content(role="user", parts=[types.Part.from_text(text=message)])
+    )
+
+    # 4. Request the response from Gemini
+    # We use 'gemini-2.5-flash' as it is fast, powerful, and ideal for chat
+    response = client.models.generate_content(
+        model='gemini-2.5-flash',
+        contents=formatted_contents,
+        config=config
+    )
+    
+    # 5. Return the text response
+    return response.text
+
+# 6. Create and launch the Gradio ChatInterface
+# We set type="messages" to match the modern history format used above
+chatbot = gr.ChatInterface(
+    google_chatbot_response, 
+    type="messages", 
+    title="My First Google Gemini Chatbot"
+)
+
+chatbot.launch()