HI all
Question : Prompt engineering code in Python which accepts some prompt and get the response from Gemini using the API key
Answer code
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from google import genai
from google.genai.errors import APIError
# NOTE: Hardcoding API keys is generally discouraged in production environments
# for security reasons. Environment variables or secret management tools are preferred.
# This is done here only to fulfill the specific request.
HARDCODED_API_KEY ="Your API code generated from Google AI Studio"
def initialize_gemini(api_key: str):
"""Initializes the Gemini client using the hardcoded API key."""
try:
# Pass the API key directly to the Client constructor
client = genai.Client(api_key=api_key)
return client
except Exception as e:
# Note: If the API key is invalid or permissions are wrong, an error
# might be raised here or during the first API call.
print(f"Error initializing client: {e}")
return None
def run_prompt_engineering_lesson(client):
"""
Demonstrates prompt engineering using a Few-Shot Prompting technique.
The goal is to teach the model a new, specific output format.
"""
if not client:
print("Cannot run lesson because the Gemini client failed to initialize.")
return
print("--- 📚 Prompt Engineering Lesson: Few-Shot Prompting ---")
# --- 1. The Prompt ---
# We provide a few examples ("shots") to guide the model's behavior.
# The goal is to make it translate casual terms into formal business language.
prompt = """
**Instructions:** You are an expert business communication consultant.
Your task is to translate casual, everyday phrases into formal, professional business language.
**Examples (Few-Shots):**
1. Casual: "We messed up the schedule."
Formal: "We encountered an unforeseen discrepancy in the project timeline."
2. Casual: "Can you email me the slides?"
Formal: "Kindly forward the presentation deck via electronic mail."
3. Casual: "Let's catch up later today."
Formal: "I propose we schedule a debriefing session later this afternoon."
**Your Turn (The Query):**
4. Casual: "The project's going nowhere fast."
Formal:"""
prompt=input('Enter a query')
print("\n[Input Prompt Sent to Gemini Model]:")
print("----------------------------------------------------------------")
print(prompt.strip())
print("----------------------------------------------------------------")
# --- 2. API Call ---
# We use a powerful model like gemini-2.5-flash
try:
response = client.models.generate_content(
model='gemini-2.5-flash',
contents=prompt,
# Adjusting temperature for more consistent responses.
config={"temperature": 0.2}
)
# --- 3. Output ---
print("\n[Model Response Received]:")
print("----------------------------------------------------------------")
# We process the response text to extract only the formal translation
full_text = response.text
if "Formal:" in full_text:
# Attempt to split to get the translation following the last "Formal:" marker
formal_translation = full_text.split("Formal:")[-1].strip()
else:
# If the model only returned the answer, just strip it
formal_translation = full_text.strip()
print(formal_translation)
print("----------------------------------------------------------------")
print("\n**✨ Prompt Engineering Concept Demonstrated: Few-Shot Learning.**")
print("By providing examples, we taught the model a specific format (Casual -> Formal) without explicit coding.")
except APIError as e:
print(f"\nAn API error occurred: {e}")
print("Check your API key and ensure you haven't exceeded the free tier quota.")
except Exception as e:
print(f"\nAn unexpected error occurred: {e}")
if __name__ == "__main__":
gemini_client = initialize_gemini(HARDCODED_API_KEY)
if gemini_client:
run_prompt_engineering_lesson(gemini_client)
