Update app.py

app.py

@@ -16,7 +16,9 @@ def generate_embeddings(texts):
     # Tokenize the Arabic text for ARAT5
     inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=512)
     with torch.no_grad():
+        # Use ARAT5 to generate embeddings
         outputs = model.encoder(input_ids=inputs['input_ids'])
+    # Extract the embeddings (mean of hidden states for simplicity)
     return outputs.last_hidden_state.mean(dim=1).numpy()
 
 # Function to process the CSV file and return emotion and topic model
@@ -28,10 +30,14 @@ def process_file(uploaded_file):
     st.write("CSV Loaded Successfully!")
     st.write(f"Data Preview: {df.head()}")
     
-    # Preprocess the text: assuming the CSV has a 'text' column
-    texts = df['text'].dropna().tolist()  # Modify this according to your column name
-
-    # Emotion Classification: Classify emotions for each text (Arabic)
+    # Ensure 'date' column is in datetime format and extract the year
+    df['date'] = pd.to_datetime(df['date'], errors='coerce')  # Replace 'date' with your actual column name
+    df['year'] = df['date'].dt.year
+    
+    # Modify this to use the 'poem' column that contains the Arabic poems
+    texts = df['poem'].dropna().tolist()  # Replace 'poem' with your actual column name
+    
+    # Emotion Classification: Classify emotions for each poem (Arabic)
     emotions = [emotion_classifier(text)[0]['label'] for text in texts]
     df['emotion'] = emotions
     
@@ -41,19 +47,61 @@ def process_file(uploaded_file):
     topics, _ = topic_model.fit_transform(embeddings)
     df['topic'] = topics
     
-    # Display the results
-    st.write("Emotions classified for each entry:")
-    st.write(df[['text', 'emotion', 'topic']])
-    
+    # Return the processed dataframe
     return df
 
 # Streamlit App
-st.title("Arabic Topic Modeling & Emotion Classification with ARAT5")
-st.write("Upload a CSV file to perform topic modeling and emotion classification on Arabic text.")
+st.title("Arabic Poem Topic Modeling & Emotion Classification with ARAT5")
+st.write("Upload a CSV file to perform topic modeling and emotion classification on Arabic poems.")
 
 # File upload widget
 uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
 
+# If file is uploaded, process and display results
 if uploaded_file is not None:
-    # Process the file
     result_df = process_file(uploaded_file)
+
+    # Show date selection widgets
+    st.write("### Filter by Date Range")
+    start_date = st.date_input("Start Date", value=pd.to_datetime(result_df['date'].min()))
+    end_date = st.date_input("End Date", value=pd.to_datetime(result_df['date'].max()))
+    
+    # Filter data based on selected date range
+    filtered_df = result_df[(result_df['date'] >= start_date) & (result_df['date'] <= end_date)]
+    
+    # Display filtered data
+    st.write(f"Filtered Data (Poems from {start_date} to {end_date}):")
+    st.write(filtered_df[['poet_name', 'era', 'poem', 'emotion', 'topic', 'date']])
+
+    # Create buttons to show different summaries
+    summary_type = st.radio("Select Summary Type:", 
+                            ("Emotion and Topic Summary by Date Range", 
+                             "Global Emotion and Topic Summary"))
+
+    # Display the selected summary
+    if summary_type == "Emotion and Topic Summary by Date Range":
+        st.write("Emotion and Topic Summary for Selected Date Range:")
+        
+        # Emotion Distribution in Date Range
+        emotion_counts = filtered_df['emotion'].value_counts()
+        st.write("Emotion Counts in Date Range:")
+        st.write(emotion_counts)
+        
+        # Topic Distribution in Date Range
+        topic_counts = filtered_df['topic'].value_counts()
+        st.write("Topic Counts in Date Range:")
+        st.write(topic_counts)
+        
+        # Visualize emotion distribution over the selected range (optional)
+        st.bar_chart(emotion_counts, use_container_width=True)
+        
+        # Visualize topic distribution over the selected range (optional)
+        st.bar_chart(topic_counts, use_container_width=True)
+
+    elif summary_type == "Global Emotion and Topic Summary":
+        st.write("Global Emotion and Topic Summary (All Poems):")
+        global_emotion_count = result_df['emotion'].value_counts().to_dict()
+        global_topic_count = result_df['topic'].value_counts().to_dict()
+        
+        st.write(f"Emotion Distribution: {global_emotion_count}")
+        st.write(f"Topic Distribution: {global_topic_count}")