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Create app.py
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app.py
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import gradio as gr
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import base64
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from PIL import Image
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import torch
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"Qwen/Qwen2-VL-2B-Instruct",
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torch_dtype="auto",
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device_map="auto",
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)
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# Function to encode images into base64
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def encode_images(image_paths):
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base64_images = []
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for image_path in image_paths:
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with open(image_path, "rb") as image_file:
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base64_image = base64.b64encode(image_file.read()).decode("utf-8")
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base64_images.append(f"data:image/jpeg;base64,{base64_image}")
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return base64_images
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# Function to resize images to a uniform shape
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def resize_images(image_paths, target_size=(224, 224)):
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resized_images = []
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for image_path in image_paths:
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img = Image.open(image_path)
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img_resized = img.resize(target_size) # Resize image to target size
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resized_images.append(img_resized)
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return resized_images
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def generate_testing_instructions(images, context):
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# Resize all images to a uniform shape (e.g., 224x224)
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resized_images = resize_images(images)
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# Encode resized images to base64
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base64_images = encode_images(images)
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# Prepare messages with the base64-encoded images
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": base64_image}},
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{"type": "text", "text": '''
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You're a helpful assistant that creates comprehensive testing instructions. Each test case should include the following details:
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- **Description**: A brief explanation of the feature being tested.
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- **Pre-conditions**: The required setup or state of the app before beginning the test.
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- **Testing Steps**: A clear, step-by-step guide for performing the test, including any user interactions or actions.
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- **Expected Result**: The outcome you should observe if the feature is functioning correctly.
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Please demonstrate your approach using the following features of a mobile app:
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1. **Source, Destination, and Date Selection**: The user selects their departure location, destination, and the travel date.
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2. **Bus Selection**: Displays a list of available buses, allowing the user to choose a specific one.
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3. **Seat Selection**: Enables the user to select seats on the chosen bus.
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4. **Pick-up and Drop-off Point Selection**: Allows the user to specify the starting point and final stop for the trip.
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5. **Offers**: Showcases available promotions and discounts.
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6. **Filters**: Options to filter buses by time, price, or other preferences.
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7. **Bus Information**: Provides details about the selected bus, including amenities, photos, and reviews.
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'''},
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{"type": "text", "text": context}
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]
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}
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for base64_image in base64_images
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]
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text_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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# Create input tensors
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inputs = processor(
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text=[text_prompt],
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images=resized_images, # Use resized images for model input
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padding=True,
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return_tensors="pt"
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)
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# Move tensors to GPU if available
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inputs = inputs.to(device)
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# Generate output
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output_ids = model.generate(**inputs, max_new_tokens=1024)
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generated_ids = [
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output_ids[len(input_ids):]
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for input_ids, output_ids in zip(inputs.input_ids, output_ids)
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]
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# Decode the output text
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output_text = processor.batch_decode(
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
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)
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return output_text
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# Create the Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown("## App Testing Instructions Generator")
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with gr.Row():
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context = gr.Textbox(label="Optional Context", placeholder="Add any specific instructions or questions...")
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image_upload = gr.File(label="Upload Screenshots (PNG or JPG)", type="filepath", file_count="multiple")
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output = gr.Textbox(label="Generated Testing Instructions", placeholder="The instructions will appear here...")
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button = gr.Button("Describe Testing Instructions")
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# Action on button click
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button.click(
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generate_testing_instructions,
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inputs=[image_upload, context],
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outputs=output
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)
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# Launch the Gradio app
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demo.launch(debug=True)
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