app.py

app.py

@@ -2,41 +2,52 @@ import torch
 import gradio as gr
 from transformers import pipeline
 
-# Initialize the summarization pipeline (without float16 for CPU)
+# Initialize the summarization pipeline
 pipe = pipeline("summarization", model="Falconsai/text_summarization")
 
-# Store the history
-history = []
+# Store chat history as a list of tuples [(user_input, summary), ...]
+chat_history = []
 
 # Define the summarize function
-def summarize(input, clear_history=False):
-    # Clear history if the flag is set
+def summarize(input_text, clear_history=False):
+    global chat_history
+
+    # Clear history if requested
     if clear_history:
-        history.clear()
-        return "History cleared!"
+        chat_history = []
+        return chat_history
 
-    # Get the summary
-    output = pipe(input)
+    # Generate the summary
+    output = pipe(input_text)
     summary = output[0]['summary_text']
-    
-    # Store the summary in history
-    history.append(summary)
-    
-    # Return the summary along with the history
-    return summary, history
+
+    # Append the user's input and the summary to chat history
+    chat_history.append(("User: " + input_text, "Summarizer: " + summary))
+
+    # Return the updated chat history
+    return chat_history
 
 # Define the Gradio interface
-iface = gr.Interface(
-    fn=summarize,
-    inputs=[
-        gr.Textbox(lines=10, placeholder="Enter text to summarize here..."),
-        gr.Checkbox(label="Clear history", value=False)  # Checkbox to clear history
-    ],
-    outputs=["text", "json"],  # Show summary and history in json format
-    title="Text Summarizer",
-    description="Enter a long piece of text, and the summarizer will provide a concise summary. You can also clear the history of summaries."
-)
+with gr.Blocks() as interface:
+    # Title and description
+    gr.Markdown("# ChatGPT-like Text Summarizer")
+    gr.Markdown("Enter a long piece of text, and the summarizer will provide a concise summary. History will appear like a chat interface.")
+    
+    # Input section
+    with gr.Row():
+        input_text = gr.Textbox(lines=10, placeholder="Enter text to summarize here...", label="Input Text")
+        clear_history_btn = gr.Button("Clear History")
+
+    # Chatbot-style output
+    chatbot = gr.Chatbot(label="History")
+
+    # Submit button for summarization
+    submit_button = gr.Button("Summarize")
+
+    # Functionality for buttons
+    submit_button.click(summarize, inputs=[input_text, gr.State(False)], outputs=chatbot)
+    clear_history_btn.click(summarize, inputs=["", gr.State(True)], outputs=chatbot)
 
 # Launch the interface
 if __name__ == "__main__":
-    iface.launch()
+    interface.launch()