improve

app.py

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+import os
+from datetime import datetime
+import json
+from huggingface_hub import snapshot_download
+from collections import defaultdict
+import pandas as pd
+import streamlit as st
+from datetime import datetime, timedelta
+import matplotlib.pyplot as plt
+
+user_input = st.text_input("Enter your text here:")
+
+libraries = [
+    "open-source-metrics/accelerate-dependents",
+    "open-source-metrics/hub-docs-dependents",
+    "open-source-metrics/huggingface_hub-dependents",
+    "open-source-metrics/evaluate-dependents",
+    "open-source-metrics/datasets-dependents",
+    "open-source-metrics/pytorch-image-models-dependents",
+    "open-source-metrics/tokenizers-dependents",
+    "open-source-metrics/transformers-dependents",
+    "open-source-metrics/diffusers-dependents",
+    "open-source-metrics/gradio-dependents",
+    "open-source-metrics/optimum-dependents",
+    "open-source-metrics/accelerate-dependents",
+]
+
+option = st.selectbox(
+    'Choose library',
+    libraries 
+)
+
+cached_folder = snapshot_download("open-source-metrics/transformers-dependents", repo_type="dataset")
+
+num_dependents = defaultdict(int)
+num_stars_all_dependents = defaultdict(int)
+
+def load_json_files(directory):
+    for subdir, dirs, files in os.walk(directory):
+        for file in files:
+            if file.endswith('.json'):
+                file_path = os.path.join(subdir, file)
+                date = "_".join(file_path.split(".")[-2].split("/")[-3:])
+                with open(file_path, 'r') as f:
+                    data = json.load(f)
+                    # Process the JSON data as needed
+                    if "name" in data and "stars" in data:
+                        num_dependents[date] = len(data["name"])
+                        num_stars_all_dependents[date] = sum(data["stars"])
+
+# Replace 'your_directory_path' with the path to the directory containing your '11' and '12' folders
+load_json_files(cached_folder)
+
+def sort_dict_by_date(d):
+    # Convert date strings to datetime objects and sort
+    sorted_tuples = sorted(d.items(), key=lambda x: datetime.strptime(x[0], '%Y_%m_%d'))
+    # Convert back to dictionary if needed
+    return defaultdict(int, sorted_tuples)
+
+def remove_incorrect_entries(data):
+    # Convert string dates to datetime objects for easier comparison
+    sorted_data = sorted(data.items(), key=lambda x: datetime.strptime(x[0], '%Y_%m_%d'))
+    
+    # Initialize a new dictionary to store the corrected data
+    corrected_data = defaultdict(int)
+    
+    # Variable to keep track of the number of dependents on the previous date
+    previous_dependents = None
+
+    for date, dependents in sorted_data:
+        # If the current number of dependents is not less than the previous, add it to the corrected data
+        if previous_dependents is None or dependents >= previous_dependents:
+            corrected_data[date] = dependents
+            previous_dependents = dependents
+
+    return corrected_data
+
+def interpolate_missing_dates(data):
+    # Convert string dates to datetime objects
+    temp_data = {datetime.strptime(date, '%Y_%m_%d'): value for date, value in data.items()}
+    
+    # Find the min and max dates to establish the range
+    min_date, max_date = min(temp_data.keys()), max(temp_data.keys())
+
+    # Generate a date range
+    current_date = min_date
+    while current_date <= max_date:
+        # If the current date is missing
+        if current_date not in temp_data:
+            # Find previous and next dates that are present
+            prev_date = current_date - timedelta(days=1)
+            next_date = current_date + timedelta(days=1)
+            while prev_date not in temp_data:
+                prev_date -= timedelta(days=1)
+            while next_date not in temp_data:
+                next_date += timedelta(days=1)
+
+            # Linear interpolation
+            prev_value = temp_data[prev_date]
+            next_value = temp_data[next_date]
+            interpolated_value = prev_value + ((next_value - prev_value) * ((current_date - prev_date) / (next_date - prev_date)))
+            temp_data[current_date] = interpolated_value
+
+        current_date += timedelta(days=1)
+
+    # Convert datetime objects back to string format
+    interpolated_data = defaultdict(int, {date.strftime('%Y_%m_%d'): int(value) for date, value in temp_data.items()})
+    
+    return interpolated_data
+
+num_dependents = remove_incorrect_entries(num_dependents)
+num_stars_all_dependents = remove_incorrect_entries(num_stars_all_dependents)
+
+num_dependents = interpolate_missing_dates(num_dependents)
+num_stars_all_dependents = interpolate_missing_dates(num_stars_all_dependents)
+
+num_dependents = sort_dict_by_date(num_dependents)
+num_stars_all_dependents = sort_dict_by_date(num_stars_all_dependents)
+
+num_dependents_df = pd.DataFrame(list(num_dependents.items()), columns=['Date', 'Value'])
+num_cum_stars_df = pd.DataFrame(list(num_stars_all_dependents.items()), columns=['Date', 'Value'])
+
+num_dependents_df['Date'] = pd.to_datetime(num_dependents_df['Date'], format='%Y_%m_%d')
+num_cum_stars_df['Date'] = pd.to_datetime(num_cum_stars_df['Date'], format='%Y_%m_%d')
+
+num_dependents_df.set_index('Date', inplace=True)
+num_dependents_df = num_dependents_df.resample('D').asfreq()
+num_dependents_df['Value'] = num_dependents_df['Value'].interpolate()
+
+num_cum_stars_df.set_index('Date', inplace=True)
+num_cum_stars_df = num_cum_stars_df.resample('D').asfreq()
+num_cum_stars_df['Value'] = num_cum_stars_df['Value'].interpolate()
+
+# Plotting
+plt.figure(figsize=(10, 6))
+plt.plot(num_dependents_df.index, num_dependents_df['Value'], marker='o')
+plt.xlabel('Date')
+plt.ylabel('Number of Dependents')
+plt.title('Dependencies History')
+
+# Display in Streamlit
+st.pyplot(plt)

requirements.txt

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+pandas
+matplotlib