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app/data_processing/data_processing.py

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+
+from bs4 import BeautifulSoup
+import requests
+
+def extract_data_from_html(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    return soup.get_text()

app/main.py

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+
+import gradio as gr
+from app.data_processing import extract_data_from_html
+from app.model_inference import run_inference
+from app.report_generation import generate_report
+from app.visualizations import plot_trends
+
+# Пример использования
+def process_data_and_generate_report(url):
+    raw_data = extract_data_from_html(url)
+    analysis_results = run_inference(raw_data)
+    report = generate_report(analysis_results)
+    plot = plot_trends(analysis_results)
+    return report, plot
+
+iface = gr.Interface(fn=process_data_and_generate_report, inputs="text", outputs=["text", "plot"])
+iface.launch()

app/model_inference/model_inference.py

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+
+from transformers import pipeline
+
+def run_inference(text):
+    nlp_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
+    return nlp_pipeline(text)

app/report_generation/report_generation.py

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+
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+def generate_report(data):
+    input_text = "Generate a detailed report about the following: " + str(data)
+    model = GPT2LMHeadModel.from_pretrained("gpt2")
+    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=150)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)

app/visualizations/visualizations.py

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+
+import matplotlib.pyplot as plt
+
+def plot_trends(data):
+    models = [item['model'] for item in data]
+    prices = [float(item['price'].replace('₴', '').strip()) for item in data]
+    plt.plot(models, prices, marker='o', color='b')
+    plt.title('Price Trends of Products')
+    plt.xlabel('Models')
+    plt.ylabel('Price')
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    return plt

config.py

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+
+# Конфигурации проекта
+DATA_PATH = "data/raw_data/"
+PROCESSED_DATA_PATH = "data/processed_data/"
+MODEL_NAME = "bert-large-cased"
+HUGGINGFACE_TOKEN = "your_huggingface_api_token"

requirements.txt

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-transformers==4.30.0
-gradio==3.24.1
-matplotlib==3.7.1
-requests==2.28.2
-beautifulsoup4==4.11.1
+
+transformers==4.30.0
+gradio==3.24.1
+matplotlib==3.7.1
+requests==2.28.2
+beautifulsoup4==4.11.1