Spaces:
Running
Running
File size: 5,600 Bytes
821e9b3 3f8b483 5521e44 821e9b3 9ed8b92 5521e44 9ed8b92 5521e44 821e9b3 3f8b483 821e9b3 3f8b483 821e9b3 3f8b483 5521e44 821e9b3 5521e44 821e9b3 5521e44 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 5521e44 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 3f8b483 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 5521e44 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 9ed8b92 821e9b3 5521e44 821e9b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import os
import re
import json
import gradio as gr
from openai import OpenAI
# Initialize the OpenAI client with the API key from environment variables.
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
# In-memory storage to track submitted emails (not persistent; resets on app restart).
submitted_emails = set()
def get_evaluation_questions():
"""
Loads evaluation questions and expected answers from environment variables.
Expected environment variable names are:
- TEST_QUESTION_1: a JSON array of user query strings.
- TEST_EXPECTED: a JSON array of JSON-like strings representing the expected outputs.
Both lists must be of equal length.
"""
questions_str = os.environ.get("TEST_QUESTION_1")
expected_str = os.environ.get("TEST_EXPECTED")
if not questions_str or not expected_str:
return []
try:
questions_list = json.loads(questions_str)
expected_list = json.loads(expected_str)
except Exception as e:
print(f"Error parsing evaluation questions: {str(e)}")
return []
if len(questions_list) != len(expected_list):
print("Mismatch in length: questions list and expected answers list must have the same length.")
return []
return [{"question": q, "expected": e} for q, e in zip(questions_list, expected_list)]
# Load the evaluation questions once at startup.
EVALUATION_QUESTIONS = get_evaluation_questions()
def sanitize_input(text):
"""
Sanitizes input to prevent harmful content and limits its length.
"""
# Allow alphanumerics and some punctuation, then truncate to 500 characters.
clean_text = re.sub(r"[^a-zA-Z0-9\s.,!?@:\-]", "", text)
return clean_text.strip()[:500]
def validate_email(email):
"""
Validates that the provided email is in a valid format.
Returns True if valid, False otherwise.
"""
email_regex = r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$"
return re.match(email_regex, email) is not None
def submit_prompt(email, name, system_prompt):
"""
Handles user submission:
- Validates email format.
- Checks if the email has already been used for submission.
- Evaluates the system prompt against predefined test questions.
- Prevents multiple submissions from the same email.
Returns the evaluation results or an error message if the submission is invalid.
"""
# Validate email format.
if not validate_email(email):
return "Invalid email address. Please enter a valid email."
# Check if this email has already been used for submission.
if email in submitted_emails:
return f"Submission already received for {email}. You can only submit once."
# Sanitize inputs.
email = sanitize_input(email)
name = sanitize_input(name)
system_prompt = sanitize_input(system_prompt)
score = 0
responses = []
for item in EVALUATION_QUESTIONS:
question = item["question"]
expected = item["expected"]
try:
# Use the new client-based API for chat completions.
response = client.chat.completions.create(
model="gpt-4o-mini", # Ensure this identifier matches the deployed model.
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": question}
]
)
# Extract the answer from the response object.
answer = response.choices[0].message.content.strip()
except Exception as e:
answer = f"Error during OpenAI API call: {str(e)}"
# Simple evaluation: check if the expected output is a substring of the answer (case-insensitive).
if expected.lower() in answer.lower():
score += 1
verdict = "Correct"
else:
verdict = "Incorrect"
responses.append(
f"Question: {question}\n"
f"Answer: {answer}\n"
f"Expected: {expected}\n"
f"Result: {verdict}\n"
)
result_details = "\n".join(responses)
# Record this email as having submitted their prompt.
submitted_emails.add(email)
return (
f"Thank you for your submission, {name}!\n\n"
f"Your evaluation score is {score} out of {len(EVALUATION_QUESTIONS)}.\n\nDetails:\n{result_details}"
)
def build_interface():
"""
Constructs the Gradio interface with a submission button and single-submission mechanism.
"""
with gr.Blocks() as demo:
gr.Markdown("# GPT-4o Mini Prompt Submission")
gr.Markdown(
"Please enter your details and submit your system prompt below. "
"You can only submit once."
)
email_input = gr.Textbox(label="Email", placeholder="your.email@example.com")
name_input = gr.Textbox(label="Name", placeholder="Your name")
system_prompt_input = gr.Textbox(
label="System Prompt",
placeholder="Enter your system prompt here...",
lines=6,
)
submit_button = gr.Button("Submit")
output_text = gr.Textbox(label="Results", lines=15)
submit_button.click(
fn=submit_prompt,
inputs=[email_input, name_input, system_prompt_input],
outputs=output_text,
)
return demo
if __name__ == "__main__":
interface = build_interface()
# Launch the app on 0.0.0.0 so it is accessible externally (e.g., in a container).
interface.launch(server_name="0.0.0.0", server_port=7860)
|