Update app.py

app.py

@@ -8,144 +8,6 @@ from utils import load_prompt, setup_gemini_client
 from configs.responses import SummaryResponses
 from google.genai import types
 
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ai-studio-api-key", type=str, default=os.getenv("GEMINI_API_KEY"))
-    parser.add_argument("--vertexai", action="store_true", default=False)
-    parser.add_argument("--vertexai-project", type=str, default="gcp-ml-172005")
-    parser.add_argument("--vertexai-location", type=str, default="us-central1")
-    parser.add_argument("--model", type=str, default="gemini-2.0-flash", choices=["gemini-1.5-flash", "gemini-2.0-flash", "gemini-2.0-flash-001"])
-    parser.add_argument("--seed", type=int, default=2025)
-    parser.add_argument("--prompt-tmpl-path", type=str, default="configs/prompts.toml")
-    parser.add_argument("--css-path", type=str, default="statics/styles.css")
-    args = parser.parse_args()
-    return args
-
-def find_attached_file(filename, attached_files):
-    for file in attached_files:
-        if file['name'] == filename:
-            return file
-    return None
-
-async def echo(message, history, state, persona):
-    attached_file = None
-    system_instruction = Template(prompt_tmpl['summarization']['system_prompt']).safe_substitute(persona=persona)
-    
-    if message['files']:
-        path_local = message['files'][0]
-        filename = os.path.basename(path_local)
-
-        attached_file = find_attached_file(filename, state["attached_files"])
-        if attached_file is None: 
-            path_gcp = await client.files.upload(path=path_local)
-            state["attached_files"].append({
-                "name": filename,
-                "path_local": path_local,
-                "gcp_entity": path_gcp,
-                "path_gcp": path_gcp.name,
-                "mime_type=": path_gcp.mime_type,
-                "expiration_time": path_gcp.expiration_time,
-            })
-            attached_file = path_gcp
-
-    user_message = [message['text']]
-    if attached_file: user_message.append(attached_file)
-
-    chat_history = state['messages']
-    chat_history = chat_history + user_message
-    state['messages'] = chat_history
-
-    response_chunks = ""
-    model_content_stream = await client.models.generate_content_stream(
-    model=args.model, 
-    contents=state['messages'], 
-    config=types.GenerateContentConfig(
-        system_instruction=system_instruction, seed=args.seed
-    ),
-)
-    async for chunk in model_content_stream:
-        response_chunks += chunk.text
-        # when model generates too fast, Gradio does not respond that in real-time.
-        await asyncio.sleep(0.1)
-        yield (
-            response_chunks, 
-            state, 
-            state['summary_diff_history'][-1] if len(state['summary_diff_history']) > 1 else "",
-            state['summary_history'][-1] if len(state['summary_history']) > 1 else "",
-            gr.Slider(
-                visible=False if len(state['summary_history']) <= 1 else True, 
-                interactive=False if len(state['summary_history']) <= 1 else True, 
-            ),
-        )        
-    
-    # make summary
-    response = await client.models.generate_content(
-        model=args.model,
-        contents=[
-            Template(
-                prompt_tmpl['summarization']['prompt']
-            ).safe_substitute(
-                previous_summary=state['summary'], 
-                latest_conversation=str({"user": message['text'], "assistant": response_chunks})
-            )
-        ],
-        config=types.GenerateContentConfig(
-            system_instruction=system_instruction, 
-            seed=args.seed,
-            response_mime_type='application/json', 
-            response_schema=SummaryResponses
-        )
-    )
-
-    prev_summary = state['summary_history'][-1] if len(state['summary_history']) >= 1 else ""
-
-    state['summary'] = (
-        response.parsed.summary 
-        if getattr(response.parsed, "summary", None) is not None 
-        else response.text
-    )
-    state['summary_history'].append(
-        response.parsed.summary 
-        if getattr(response.parsed, "summary", None) is not None 
-        else response.text
-    )
-    state['summary_diff_history'].append(
-        [
-            (token[2:], token[0] if token[0] != " " else None)
-            for token in Differ().compare(prev_summary, state['summary'])
-        ]
-    )
-
-    yield (
-        response_chunks, 
-        state, 
-        state['summary_diff_history'][-1],
-        state['summary_history'][-1],
-        gr.Slider(
-            maximum=len(state['summary_history']),
-            value=len(state['summary_history']),
-            visible=False if len(state['summary_history']) == 1 else True, interactive=True
-        ),
-    )
-
-def change_view_toggle(view_toggle):
-    if view_toggle == "Diff":
-        return (
-            gr.HighlightedText(visible=True),
-            gr.Markdown(visible=False)
-        )
-    else:
-        return (
-            gr.HighlightedText(visible=False),
-            gr.Markdown(visible=True)
-        )        
-
-def navigate_to_summary(summary_num, state):
-    return (
-        state['summary_diff_history'][summary_num-1],
-        state['summary_history'][summary_num-1]
-    )
-
 def main(args):
     style_css = open(args.css_path, "r").read()
 
@@ -168,55 +30,6 @@ def main(args):
             gr.Markdown("# Adaptive Summarization")
             gr.Markdown("AdaptSum stands for Adaptive Summarization. This project focuses on developing an LLM-powered system for dynamic summarization. Instead of generating entirely new summaries with each update, the system intelligently identifies and modifies only the necessary parts of the existing summary. This approach aims to create a more efficient and fluid summarization process within a continuous chat interaction with an LLM.")
 
-        with gr.Column():
-            with gr.Accordion("Adaptively Summarized Conversation", elem_id="adaptive-summary-accordion", open=False):
-                with gr.Row(elem_id="view-toggle-btn-container"):
-                    view_toggle_btn = gr.Radio(
-                        choices=["Diff", "Markdown"],
-                        value="Markdown",
-                        interactive=True,
-                        elem_id="view-toggle-btn"
-                    )
-
-                summary_diff = gr.HighlightedText(
-                    label="Summary so far",
-                    # value="No summary yet. As you chat with the assistant, the summary will be updated automatically.",
-                    combine_adjacent=True,
-                    show_legend=True,
-                    color_map={"-": "red", "+": "green"},
-                    elem_classes=["summary-window"],
-                    visible=False
-                )
-
-                summary_md = gr.Markdown(
-                    label="Summary so far",
-                    value="No summary yet. As you chat with the assistant, the summary will be updated automatically.",
-                    elem_classes=["summary-window"],
-                    visible=True
-                )
-
-                summary_num = gr.Slider(label="summary history", minimum=1, maximum=1, step=1, show_reset_button=False, visible=False)
-
-            view_toggle_btn.change(change_view_toggle, inputs=[view_toggle_btn], outputs=[summary_diff, summary_md])
-            summary_num.release(navigate_to_summary, inputs=[summary_num, state], outputs=[summary_diff, summary_md])
-        
-        with gr.Column("persona-dropdown-container", elem_id="persona-dropdown-container"):
-            persona = gr.Dropdown(
-                ["expert", "novice", "regular practitioner", "high schooler"], 
-                label="Summary Persona", 
-                info="Control the tonality of the conversation.",
-                min_width="auto",
-            )        
-
-        with gr.Column("chat-window", elem_id="chat-window"):
-            gr.ChatInterface(
-                multimodal=True,
-                type="messages", 
-                fn=echo, 
-                additional_inputs=[state, persona],
-                additional_outputs=[state, summary_diff, summary_md, summary_num],
-            )
-
     return demo
 
 if __name__ == "__main__":