Update app.py

app.py

@@ -1,7 +1,8 @@
-import streamlit as st
-import praw  # Reddit's API
+import matplotlib.pyplot as plt
 import pandas as pd
+import praw  # Reddit's API
 import re  # Regular expression module
+import streamlit as st
 import time
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 
@@ -83,7 +84,7 @@ if st.button("Scrape Reddit"):
     
     # Convert Date to datetime, sort descending and reset index
     df['Date'] = pd.to_datetime(df['Date'], unit='s')
-    df = df.sort_values(by="Date", ascending=False).reset_index(drop=True)
+    df = df.sort_values(by="Date", ascending=True).reset_index(drop=True)
 
     progress_text.text(f"Collected {len(df)} valid posts.")
     st.session_state["df"] = df
@@ -156,21 +157,46 @@ if st.button("Sentiment Analysis"):
 
     
     with st.spinner("Doing Sentiment Analysis..."):
-        # Apply sentiment analysis to Title directly (assuming Title is short)
-        df['title_sentiment'] = df['Title'].apply(lambda x: safe_sentiment(preprocess_text(x)) if x else None)
-        
-        # Apply sentiment analysis to Detail by splitting into token-limited chunks and accumulating scores
-        df['detail_sentiment'] = df['Detail'].apply(lambda x: analyze_detail(x, tokenizer, sentiment_pipeline, max_tokens) if x else None)
+        with st.spinner("Title Sentiment..."):
+            # Apply sentiment analysis to Title directly (assuming Title is short)
+            df['title_sentiment'] = df['Title'].apply(lambda x: safe_sentiment(preprocess_text(x)) if x else None)
+            df["Title_sentiment_label"] = df["title_sentiment"].apply(lambda x: x["label"] if x else None)
+            df["Title_sentiment_score"] = df["title_sentiment"].apply(lambda x: x["score"] if x else None)
         
-        df["Title_sentiment_label"] = df["title_sentiment"].apply(lambda x: x["label"] if x else None)
-        df["Title_sentiment_score"] = df["title_sentiment"].apply(lambda x: x["score"] if x else None)
-        df["Detail_sentiment_label"] = df["detail_sentiment"].apply(lambda x: x["label"] if x else None)
-        df["Detail_sentiment_score"] = df["detail_sentiment"].apply(lambda x: x["score"] if x else None)
+        with st.spinner("Detail Sentiment..."):
+            # Apply sentiment analysis to Detail by splitting into token-limited chunks and accumulating scores
+            df['detail_sentiment'] = df['Detail'].apply(lambda x: analyze_detail(x, tokenizer, sentiment_pipeline, max_tokens) if x else None)
+            df["Detail_sentiment_label"] = df["detail_sentiment"].apply(lambda x: x["label"] if x else None)
+            df["Detail_sentiment_score"] = df["detail_sentiment"].apply(lambda x: x["score"] if x else None)
         
         df = df.drop(["title_sentiment", "detail_sentiment"], axis=1)
         cols = ["Title", "Title_sentiment_label", "Title_sentiment_score", \
                             "Detail", "Detail_sentiment_label", "Detail_sentiment_score", "Date"]
         df = df[cols]
         
-        st.write("Sentiment analysis complete. Top 5 results:")
-        st.dataframe(df.head())
+        st.session_state["df"] = df
+
+
+
+if st.button("Draw Graph"):
+    df = st.session_state.get("df")
+
+    # Plot Title's sentiment_score
+    fig1, ax1 = plt.subplots(figsize=(10, 5))
+    ax1.plot(df["Date"], df["Title_sentiment_score"], marker="o", label="Title Sentiment Score")
+    ax1.set_title("Title Sentiment Score Over Time")
+    ax1.set_xlabel("Time")
+    ax1.set_ylabel("Sentiment Score")
+    ax1.legend()
+    plt.xticks(rotation=45)
+    st.pyplot(fig1)
+
+    # Plot Detail's sentiment_score
+    fig2, ax2 = plt.subplots(figsize=(10, 5))
+    ax2.plot(df["Date"], df["Detail_sentiment_score"], marker="△", label="Detail Sentiment Score")
+    ax2.set_title("Detail Sentiment Score Over Time")
+    ax2.set_xlabel("Time")
+    ax2.set_ylabel("Sentiment Score")
+    ax2.legend()
+    plt.xticks(rotation=45)
+    st.pyplot(fig2)