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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import gradio as gr | |
| import random | |
| checkpoint = "mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| client = InferenceClient(checkpoint) | |
| def format_prompt(raw_Prompt): | |
| better_prompt = f"[INST] {raw_Prompt} [/INST]" | |
| return better_prompt | |
| def findShops(target_platform, target_region, target_field, target_rangeFrom, target_rangeTo, target_crossSelling, target_similarArticles, target_amount): | |
| requestPrompt = f""" | |
| Search for {target_amount} Shops that use the platform {target_platform}. | |
| Make sure the recommended shops are located in {target_region}. | |
| The websites of the recommended shops should have between {target_rangeFrom} and {target_rangeTo} monthly visitors.""" | |
| crossSellingPrompt = """ | |
| The recommended shops should be potential customers for a software that improves the cross-selling on their website.""" | |
| similarArticlesPrompt = """ | |
| The recommended shops should be potential customers for a software that can recommend similar products to the customers.""" | |
| fieldPrompt = f""" | |
| Focus on shops that have a focus on the topic'{target_field}'.""" | |
| stylePrompt = """ | |
| Leave an empty line between your recommendations. | |
| Present your results in a list with the names of the shops as the headings.""" | |
| fullPrompt = requestPrompt | |
| if target_crossSelling: | |
| fullPrompt += crossSellingPrompt | |
| if target_similarArticles: | |
| fullPrompt += similarArticlesPrompt | |
| if target_field != 'All': | |
| fullPrompt += fieldPrompt | |
| fullPrompt += stylePrompt | |
| generate_kwargs = dict( | |
| temperature=0.2, | |
| max_new_tokens=10000, | |
| top_p=0.9, | |
| repetition_penalty=1.0, | |
| do_sample=True, | |
| seed=random.randint(0, 100000), | |
| ) | |
| stream = client.text_generation(format_prompt(fullPrompt), | |
| **generate_kwargs, | |
| stream=True, | |
| details=True, | |
| return_full_text=False) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| output = output.removesuffix('</s>') | |
| return output | |
| demo = gr.Blocks(title="Shop finder", | |
| theme=gr.themes.Soft(primary_hue="sky")) as app: | |
| gr.Markdown( | |
| "## Shop finder") | |
| gr.Markdown( | |
| "Choose the right settings and click **FIND** to search for shops.") | |
| with gr.Group(): | |
| gr.Markdown("**FILTERS**") | |
| with gr.Column(): | |
| platform = gr.Dropdown(choices=['Shopware', 'Shopify'], | |
| value='Shopware', | |
| interactive=True, | |
| label="PLATFORM") | |
| region = gr.Dropdown(choices=['Germany', 'England', 'France', 'Italy', 'Austria'], | |
| value='Germany', | |
| interactive=True, | |
| label="REGION") | |
| field = gr.Dropdown(choices=['All', 'Home & Living', 'Clothing', 'Electronics', 'Sports', 'Toys'], | |
| value='All', | |
| interactive=True, | |
| label="FIELD") | |
| with gr.Group(): | |
| gr.Markdown("**MONTHLY VISITORS**") | |
| with gr.Row(): | |
| range_from = gr.Slider(minimum=0, | |
| maximum=1_000_000, | |
| value=100, | |
| step=100, | |
| label="FROM", | |
| interactive=True) | |
| range_to = gr.Slider(minimum=0, | |
| maximum=1_000_000, | |
| value=10_000, | |
| step=100, | |
| label="TO", | |
| interactive=True) | |
| with gr.Group(): | |
| gr.Markdown("**POTENTIAL PRODUCT**") | |
| with gr.Row(): | |
| opt_crossSelling = gr.Checkbox(value=False, | |
| label='CROSSELLING POTENTIAL', | |
| interactive=True) | |
| opt_similarArticles = gr.Checkbox(value=False, | |
| label='SIMILARITY POTENTIAL', | |
| interactive=True) | |
| numberOfShops = gr.Slider(minimum=1, | |
| maximum=50, | |
| value=10, | |
| step=1, | |
| label="AMOUNT", | |
| interactive=True) | |
| btn = gr.Button("FIND") | |
| with gr.Group(): | |
| gr.Markdown("**RESULTS**") | |
| out = gr.Textbox(lines=10, | |
| max_lines=100, | |
| label='POTENTIAL SHOPS') | |
| btn.click(fn=findShops, | |
| inputs=[platform, region, field, | |
| range_from, range_to, | |
| opt_crossSelling, | |
| opt_similarArticles, | |
| numberOfShops], | |
| outputs=out) | |
| demo.launch(show_api=False) | |