Update app.py

app.py

@@ -1,235 +1,3 @@
-# import gradio as gr
-# import schedule
-# import time
-# import datetime
-# import praw
-# import joblib
-# import torch
-# import scipy.sparse as sp
-# import torch.nn as nn
-# import pandas as pd
-# import re
-# import numpy as np
-# import matplotlib.pyplot as plt
-# from scipy.interpolate import make_interp_spline
-# from transformers import AutoTokenizer
-# import matplotlib.font_manager as fm
-
-# # Load models and data (your existing code)
-# autovectorizer = joblib.load('AutoVectorizer.pkl')
-# autoclassifier = joblib.load('AutoClassifier.pkl')
-# MODEL = "cardiffnlp/xlm-twitter-politics-sentiment"
-# tokenizer = AutoTokenizer.from_pretrained(MODEL)
-
-# class ScorePredictor(nn.Module):
-#     # ... (Your ScorePredictor class)
-#     def __init__(self, vocab_size, embedding_dim=128, hidden_dim=256, output_dim=1):
-#         super(ScorePredictor, self).__init__()
-#         self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
-#         self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
-#         self.fc = nn.Linear(hidden_dim, output_dim)
-#         self.sigmoid = nn.Sigmoid()
-
-#     def forward(self, input_ids, attention_mask):
-#         embedded = self.embedding(input_ids)
-#         lstm_out, _ = self.lstm(embedded)
-#         final_hidden_state = lstm_out[:, -1, :]
-#         output = self.fc(final_hidden_state)
-#         return self.sigmoid(output)
-
-# score_model = ScorePredictor(tokenizer.vocab_size)
-# score_model.load_state_dict(torch.load("score_predictor.pth"))
-# score_model.eval()
-
-# sentiment_model = joblib.load('sentiment_forecast_model.pkl')
-
-# reddit = praw.Reddit(
-#     client_id="PH99oWZjM43GimMtYigFvA",
-#     client_secret="3tJsXQKEtFFYInxzLEDqRZ0s_w5z0g",
-#     user_agent='MyAPI/0.0.1',
-#     check_for_async=False)
-
-# subreddits = [
-#     "florida",
-#     "ohio",
-#     # "libertarian",
-#     # "southpark",
-#     # "walkaway",
-#     # "truechristian",
-#     # "conservatives"
-# ]
-
-# # Global variables for data
-# global prediction_plot_base64
-
-# def process_data():
-#     """Fetches data, performs analysis, and generates the plot."""
-#     global prediction_plot_base64
-#     end_date = datetime.datetime.utcnow()
-#     start_date = end_date - datetime.timedelta(days=14)
-
-#     def fetch_all_recent_posts(subreddit_name, start_time, limit=500):
-#         # ... (Your fetch_all_recent_posts function)
-#         subreddit = reddit.subreddit(subreddit_name)
-#         posts = []
-
-#         try:
-#             for post in subreddit.top(limit=limit):  # Fetch recent posts
-#                 post_time = datetime.datetime.utcfromtimestamp(post.created_utc)
-#                 if post_time >= start_time:  # Filter only within last 14 days
-#                     posts.append({
-#                         "subreddit": subreddit_name,
-#                         "timestamp": post.created_utc,
-#                         "date": post_time.strftime('%Y-%m-%d %H:%M:%S'),
-#                         "post_text": post.title
-#                     })
-#         except Exception as e:
-#             print(f"Error fetching posts from r/{subreddit_name}: {e}")
-
-#         return posts
-
-#     def preprocess_text(text):
-#         # ... (Your preprocess_text function)
-#         text = text.lower()
-#         text = re.sub(r'http\S+', '', text)
-#         text = re.sub(r'[^a-zA-Z0-9\s.,!?]', '', text)
-#         text = re.sub(r'\s+', ' ', text).strip()
-#         return text
-
-#     def predict_score(text):
-#         # ... (Your predict_score function)
-#         if not text:
-#             return 0.0
-#         max_length = 512
-
-#         encoded_input = tokenizer(
-#             text.split(),
-#             return_tensors='pt',
-#             padding=True,
-#             truncation=True,
-#             max_length=max_length
-#         )
-
-#         input_ids, attention_mask = encoded_input["input_ids"], encoded_input["attention_mask"]
-#         with torch.no_grad():
-#             score = score_model(input_ids, attention_mask)[0].item()
-#         return score
-
-#     start_time = datetime.datetime.utcnow() - datetime.timedelta(days=14)
-#     all_posts = []
-#     for sub in subreddits:
-#         print(f"Fetching posts from r/{sub}")
-#         posts = fetch_all_recent_posts(sub, start_time)
-#         all_posts.extend(posts)
-#         print(f"Fetched {len(posts)} posts from r/{sub}")
-
-#     filtered_posts = []
-#     for post in all_posts:
-#         vector = autovectorizer.transform([post['post_text']])
-#         prediction = autoclassifier.predict(vector)
-#         if prediction[0] == 1:
-#             filtered_posts.append(post)
-#     all_posts = filtered_posts
-
-#     df = pd.DataFrame(all_posts)
-#     df['date'] = pd.to_datetime(df['date'])
-#     df['date_only'] = df['date'].dt.date
-#     df = df.sort_values(by=['date_only'])
-#     df['sentiment_score'] = df['post_text'].apply(predict_score)
-
-#     last_14_dates = df['date_only'].unique()
-#     num_dates = min(len(last_14_dates), 14)
-#     last_14_dates = sorted(last_14_dates, reverse=True)[:num_dates]
-
-#     filtered_df = df[df['date_only'].isin(last_14_dates)]
-#     daily_sentiment = filtered_df.groupby('date_only')['sentiment_score'].median()
-
-#     if len(daily_sentiment) < 14:
-#         mean_sentiment = daily_sentiment.mean()
-#         padding = [mean_sentiment] * (14 - len(daily_sentiment))
-#         daily_sentiment = np.concatenate([daily_sentiment.values, padding])
-#         daily_sentiment = pd.Series(daily_sentiment)
-
-#     sentiment_scores_np = daily_sentiment.values.reshape(1, -1)
-#     prediction = sentiment_model.predict(sentiment_scores_np)
-#     pred = (prediction[0])
-
-#     font_path = "AfacadFlux-VariableFont_slnt,wght[1].ttf"
-#     custom_font = fm.FontProperties(fname=font_path)
-
-#     today = datetime.date.today()
-#     days = [today + datetime.timedelta(days=i) for i in range(7)]
-#     days_str = [day.strftime('%a %m/%d') for day in days]
-
-#     xnew = np.linspace(0, 6, 300)
-#     spline = make_interp_spline(np.arange(7), pred, k=3)
-#     pred_smooth = spline(xnew)
-
-#     fig, ax = plt.subplots(figsize=(12, 7))
-#     ax.fill_between(xnew, pred_smooth, color='#244B48', alpha=0.4)
-#     ax.plot(xnew, pred_smooth, color='#244B48', lw=3, label='Forecast')
-#     ax.scatter(np.arange(7), pred, color='#244B48', s=100, zorder=5)
-
-#     ax.set_title("7-Day Political Sentiment Forecast", fontsize=22, fontweight='bold', pad=20, fontproperties=custom_font)
-#     ax.set_xlabel("Day", fontsize=16, fontproperties=custom_font)
-#     ax.set_ylabel("Negative Sentiment (0-1)", fontsize=16, fontproperties=custom_font)
-#     ax.set_xticks(np.arange(7))
-#     ax.set_xticklabels(days_str, fontsize=14, fontproperties=custom_font)
-
-# # Continue from previous app.py code
-
-#     ax.set_yticklabels([f"{tick:.2f}" for tick in ax.get_yticks()], fontsize=14, fontproperties=custom_font)
-
-#     ax.spines['top'].set_visible(False)
-#     ax.spines['right'].set_visible(False)
-#     ax.spines['left'].set_visible(False)
-#     ax.spines['bottom'].set_visible(False)
-
-#     ax.legend(fontsize=14, loc='upper right', prop=custom_font)
-#     plt.tight_layout()
-
-#     import io
-#     import base64
-#     buffer = io.BytesIO()
-#     plt.savefig(buffer, format='png')
-#     buffer.seek(0)
-#     prediction_plot_base64 = base64.b64encode(buffer.getvalue()).decode()
-#     plt.close(fig)
-
-# def display_plot():
-#     """Displays the plot in the Gradio interface."""
-#     global prediction_plot_base64
-#     if prediction_plot_base64:
-#         return f'<img src="data:image/png;base64,{prediction_plot_base64}" alt="Prediction Plot">'
-#     else:
-#         return "Processing data..."
-
-# # Initial data processing
-# process_data()
-
-# # Schedule daily refresh
-# def run_daily():
-#     process_data()
-#     print("Data refreshed at:", datetime.datetime.now())
-
-# schedule.every().day.at("00:00").do(run_daily)
-
-# def run_schedule():
-#     while True:
-#         schedule.run_pending()
-#         time.sleep(60)
-
-# import threading
-# thread = threading.Thread(target=run_schedule)
-# thread.daemon = True
-# thread.start()
-
-# # Gradio Interface
-# iface = gr.Interface(fn=display_plot, inputs=None, outputs="html")
-# iface.launch()
-
-
-
 import gradio as gr
 import schedule
 import time
@@ -246,8 +14,6 @@ import matplotlib.pyplot as plt
 from scipy.interpolate import make_interp_spline
 from transformers import AutoTokenizer
 import matplotlib.font_manager as fm
-import io
-import base64
 
 # Load models and data (your existing code)
 autovectorizer = joblib.load('AutoVectorizer.pkl')
@@ -256,6 +22,7 @@ MODEL = "cardiffnlp/xlm-twitter-politics-sentiment"
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
 class ScorePredictor(nn.Module):
+    # ... (Your ScorePredictor class)
     def __init__(self, vocab_size, embedding_dim=128, hidden_dim=256, output_dim=1):
         super(ScorePredictor, self).__init__()
         self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
@@ -273,22 +40,27 @@ class ScorePredictor(nn.Module):
 score_model = ScorePredictor(tokenizer.vocab_size)
 score_model.load_state_dict(torch.load("score_predictor.pth"))
 score_model.eval()
+
 sentiment_model = joblib.load('sentiment_forecast_model.pkl')
 
 reddit = praw.Reddit(
     client_id="PH99oWZjM43GimMtYigFvA",
     client_secret="3tJsXQKEtFFYInxzLEDqRZ0s_w5z0g",
     user_agent='MyAPI/0.0.1',
-    check_for_async=False
-)
+    check_for_async=False)
 
 subreddits = [
     "florida",
     "ohio",
+    "libertarian",
+    "southpark",
+    "walkaway",
+    "truechristian",
+    "conservatives"
 ]
 
 # Global variables for data
-prediction_plot_base64 = None  # Initialize to None
+global prediction_plot_base64
 
 def process_data():
     """Fetches data, performs analysis, and generates the plot."""
@@ -297,12 +69,14 @@ def process_data():
     start_date = end_date - datetime.timedelta(days=14)
 
     def fetch_all_recent_posts(subreddit_name, start_time, limit=500):
+        # ... (Your fetch_all_recent_posts function)
         subreddit = reddit.subreddit(subreddit_name)
         posts = []
+
         try:
-            for post in subreddit.top(limit=limit):
+            for post in subreddit.top(limit=limit):  # Fetch recent posts
                 post_time = datetime.datetime.utcfromtimestamp(post.created_utc)
-                if post_time >= start_time:
+                if post_time >= start_time:  # Filter only within last 14 days
                     posts.append({
                         "subreddit": subreddit_name,
                         "timestamp": post.created_utc,
@@ -311,9 +85,11 @@ def process_data():
                     })
         except Exception as e:
             print(f"Error fetching posts from r/{subreddit_name}: {e}")
+
         return posts
 
     def preprocess_text(text):
+        # ... (Your preprocess_text function)
         text = text.lower()
         text = re.sub(r'http\S+', '', text)
         text = re.sub(r'[^a-zA-Z0-9\s.,!?]', '', text)
@@ -321,9 +97,11 @@ def process_data():
         return text
 
     def predict_score(text):
+        # ... (Your predict_score function)
         if not text:
             return 0.0
         max_length = 512
+
         encoded_input = tokenizer(
             text.split(),
             return_tensors='pt',
@@ -331,6 +109,7 @@ def process_data():
             truncation=True,
             max_length=max_length
         )
+
         input_ids, attention_mask = encoded_input["input_ids"], encoded_input["attention_mask"]
         with torch.no_grad():
             score = score_model(input_ids, attention_mask)[0].item()
@@ -396,9 +175,56 @@ def process_data():
     ax.set_ylabel("Negative Sentiment (0-1)", fontsize=16, fontproperties=custom_font)
     ax.set_xticks(np.arange(7))
     ax.set_xticklabels(days_str, fontsize=14, fontproperties=custom_font)
+
+# Continue from previous app.py code
+
     ax.set_yticklabels([f"{tick:.2f}" for tick in ax.get_yticks()], fontsize=14, fontproperties=custom_font)
 
     ax.spines['top'].set_visible(False)
     ax.spines['right'].set_visible(False)
     ax.spines['left'].set_visible(False)
-    ax.spines['bottom'].set_visible
+    ax.spines['bottom'].set_visible(False)
+
+    ax.legend(fontsize=14, loc='upper right', prop=custom_font)
+    plt.tight_layout()
+
+    import io
+    import base64
+    buffer = io.BytesIO()
+    plt.savefig(buffer, format='png')
+    buffer.seek(0)
+    prediction_plot_base64 = base64.b64encode(buffer.getvalue()).decode()
+    plt.close(fig)
+
+def display_plot():
+    """Displays the plot in the Gradio interface."""
+    global prediction_plot_base64
+    if prediction_plot_base64:
+        return f'<img src="data:image/png;base64,{prediction_plot_base64}" alt="Prediction Plot">'
+    else:
+        return "Processing data..."
+
+# Initial data processing
+process_data()
+
+# Schedule daily refresh
+def run_daily():
+    process_data()
+    print("Data refreshed at:", datetime.datetime.now())
+
+schedule.every().day.at("00:00").do(run_daily)
+
+def run_schedule():
+    while True:
+        schedule.run_pending()
+        time.sleep(60)
+
+import threading
+thread = threading.Thread(target=run_schedule)
+thread.daemon = True
+thread.start()
+
+# Gradio Interface
+iface = gr.Interface(fn=display_plot, inputs=None, outputs="html")
+iface.launch()
+