|
import gradio as gr |
|
import cv2 |
|
from PIL import Image |
|
import google.generativeai as genai |
|
import os |
|
from dotenv import load_dotenv |
|
|
|
load_dotenv() |
|
api_key = os.getenv('api_key') |
|
genai.configure(api_key=api_key) |
|
|
|
def extract_frames(video_path, fps=30): |
|
frames = [] |
|
cap = cv2.VideoCapture(video_path) |
|
|
|
if not cap.isOpened(): |
|
raise ValueError("Error: Unable to open video file.") |
|
|
|
original_fps = cap.get(cv2.CAP_PROP_FPS) |
|
if original_fps <= 0: |
|
raise ValueError("Error: Unable to retrieve valid FPS from video file.") |
|
|
|
frame_interval = max(1, int(original_fps // fps)) |
|
frame_count = 0 |
|
success, frame = cap.read() |
|
|
|
while success: |
|
if frame_count % frame_interval == 0: |
|
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
|
pil_image = Image.fromarray(frame) |
|
frames.append(pil_image) |
|
frame_count += 1 |
|
success, frame = cap.read() |
|
|
|
cap.release() |
|
|
|
if len(frames) == 0: |
|
raise ValueError("Error: No frames extracted from the video.") |
|
|
|
return frames |
|
|
|
def generate_prompt(button_list): |
|
button_descriptions = ", ".join(button_list) |
|
return ( |
|
f"Generate structured test cases for the following UI button(s): {button_descriptions}.\n\n" |
|
"Functionality\n" |
|
"- What the button is supposed to do.\n\n" |
|
"Preconditions\n" |
|
"- Any requirements before interaction.\n\n" |
|
"Test Steps\n" |
|
"- Concise steps for executing the test.\n\n" |
|
"Expected Results\n" |
|
"- What the expected outcome should be.\n\n" |
|
"Automated Testing Tools\n" |
|
"- Recommended tools for testing.\n\n" |
|
"Format the output clearly for easy readability, using bullet points for key details." |
|
) |
|
|
|
def process_video(video_file, buttons_to_test): |
|
try: |
|
video_path = "uploaded_video.mp4" |
|
with open(video_path, "wb") as f: |
|
f.write(video_file) |
|
|
|
frames = extract_frames(video_path, fps=15) |
|
frames_to_pass = frames[::10] |
|
|
|
button_list = [button.strip() for button in buttons_to_test.split(",")] |
|
prompt = generate_prompt(button_list) |
|
|
|
frame_prompts = [frame for frame in frames_to_pass] |
|
response = genai.GenerativeModel(model_name="gemini-1.5-pro-latest").generate_content([prompt] + frame_prompts) |
|
|
|
output_text = response.text if hasattr(response, 'text') else "Error: Invalid response from the model." |
|
output_text = output_text.replace("Functionality", "📋 Functionality") |
|
output_text = output_text.replace("Preconditions", "🔍 Preconditions") |
|
output_text = output_text.replace("Test Steps", "📝 Test Steps") |
|
output_text = output_text.replace("Expected Results", "✅ Expected Results") |
|
output_text = output_text.replace("Automated Testing Tools", "🛠️ Automated Testing Tools") |
|
|
|
return output_text |
|
|
|
except Exception as e: |
|
return f"Error occurred: {str(e)}" |
|
|
|
iface = gr.Interface( |
|
fn=process_video, |
|
inputs=[ |
|
gr.File(label="Upload Video", type="binary"), |
|
gr.Textbox(label="Buttons/Functions to Test (comma-separated)", placeholder="e.g., booking, cancel"), |
|
], |
|
outputs="text", |
|
title="Video-to-Test Case Generator with Google Gemini", |
|
description="Upload a video and specify the buttons you want to test. The tool will generate structured test cases." |
|
) |
|
|
|
iface.launch(share=True) |
|
|