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Update app.py
Browse files
app.py
CHANGED
@@ -18,7 +18,7 @@ MODEL_ENDPOINTS = {
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}
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def query_model(model_name: str, messages: List[Dict[str, str]]) -> str:
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"""Query a single model with the chat history
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endpoint = MODEL_ENDPOINTS[model_name]
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headers = {
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"Authorization": f"Bearer {HF_API_KEY}",
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@@ -28,7 +28,7 @@ def query_model(model_name: str, messages: List[Dict[str, str]]) -> str:
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# Build full conversation history for context
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conversation = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
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# Model-specific prompt formatting
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model_prompts = {
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"Qwen2.5-72B-Instruct": (
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f"<|im_start|>system\nCollaborate with other experts. Previous discussion:\n{conversation}<|im_end|>\n"
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@@ -66,15 +66,16 @@ def query_model(model_name: str, messages: List[Dict[str, str]]) -> str:
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response = requests.post(endpoint, json=payload, headers=headers)
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response.raise_for_status()
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result = response.json()[0]['generated_text']
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#
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result = result.split('<|')[0] # Remove special tokens
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result = result.replace('**', '').replace('##', '')
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except Exception as e:
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return f"{model_name} error: {str(e)}"
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def respond(message: str, history: List[List[str]], session_id: str) -> Generator[str, None, None]:
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"""Handle sequential model responses with context preservation
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# Load or initialize session
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session = session_manager.load_session(session_id)
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if not isinstance(session, dict) or "history" not in session:
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@@ -131,51 +132,31 @@ def respond(message: str, history: List[List[str]], session_id: str) -> Generato
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})
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messages.append({"role": "assistant", "content": f"Llama3.3-70B-Instruct: {response3}"})
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# Save session
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session_manager.save_session(session_id, session)
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# Return final combined response
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yield
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f"π΅ **Qwen2.5-Coder-32B-Instruct**\n{response1}\n\n"
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f"π£ **Qwen2.5-72B-Instruct**\n{response2}\n\n"
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f"π‘ **Llama3.3-70B-Instruct**\n{response3}"
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)
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with gr.Blocks() as demo:
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" />
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<script defer src="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.js"></script>
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<script defer src="https://cdn.jsdelivr.net/npm/[email protected]/dist/contrib/auto-render.min.js"></script>
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<script>
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// Re-render math whenever new content is added
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document.addEventListener("DOMContentLoaded", function() {
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const observer = new MutationObserver(function(mutations) {
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for (const mutation of mutations) {
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if (mutation.type === 'childList') {
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renderMathInElement(document.body, {
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delimiters: [
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{left: "$$", right: "$$", display: true},
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{left: "$", right: "$", display: false},
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]
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});
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}
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}
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});
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observer.observe(document.body, { subtree: true, childList: true });
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});
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</script>
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""")
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gr.Markdown("## Multi-LLM Collaboration Chat (with LaTeX support)")
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with gr.Row():
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session_id = gr.State(session_manager.create_session)
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new_session = gr.Button("π New Session")
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def on_new_session():
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new_id = session_manager.create_session()
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return new_id, []
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@@ -189,7 +170,7 @@ with gr.Blocks() as demo:
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for response in respond(message, history[:-1], session_id):
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history[-1][1] = response
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yield history
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msg.submit(user, [msg, chatbot, session_id], [msg, chatbot]).then(
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bot, [chatbot, session_id], [chatbot]
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)
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}
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def query_model(model_name: str, messages: List[Dict[str, str]]) -> str:
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"""Query a single model with the chat history"""
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endpoint = MODEL_ENDPOINTS[model_name]
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headers = {
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"Authorization": f"Bearer {HF_API_KEY}",
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# Build full conversation history for context
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conversation = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
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# Model-specific prompt formatting with full history
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model_prompts = {
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"Qwen2.5-72B-Instruct": (
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f"<|im_start|>system\nCollaborate with other experts. Previous discussion:\n{conversation}<|im_end|>\n"
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response = requests.post(endpoint, json=payload, headers=headers)
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response.raise_for_status()
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result = response.json()[0]['generated_text']
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# Clean up response formatting
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result = result.split('<|')[0] # Remove any remaining special tokens
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result = result.replace('**', '').replace('##', '') # Remove markdown emphasis
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result = result.strip() # Remove leading/trailing whitespace
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return result
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except Exception as e:
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return f"{model_name} error: {str(e)}"
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def respond(message: str, history: List[List[str]], session_id: str) -> Generator[str, None, None]:
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"""Handle sequential model responses with context preservation"""
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# Load or initialize session
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session = session_manager.load_session(session_id)
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if not isinstance(session, dict) or "history" not in session:
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})
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messages.append({"role": "assistant", "content": f"Llama3.3-70B-Instruct: {response3}"})
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# Save final session state
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session_manager.save_session(session_id, session)
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# Return final combined response
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yield f"π΅ **Qwen2.5-Coder-32B-Instruct**\n{response1}\n\nπ£ **Qwen2.5-72B-Instruct**\n{response2}\n\nπ‘ **Llama3.3-70B-Instruct**\n{response3}"
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# Create the Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown("## Multi-LLM Collaboration Chat")
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with gr.Row():
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session_id = gr.State(session_manager.create_session)
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new_session = gr.Button("π New Session")
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# Add latex_delimiters to enable LaTeX rendering
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chatbot = gr.Chatbot(
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height=600,
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latex_delimiters=[
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{"left": "$", "right": "$", "display": False}, # inline math
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{"left": "$$", "right": "$$", "display": True} # display math
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]
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)
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msg = gr.Textbox(label="Message")
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def on_new_session():
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new_id = session_manager.create_session()
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return new_id, []
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for response in respond(message, history[:-1], session_id):
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history[-1][1] = response
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yield history
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msg.submit(user, [msg, chatbot, session_id], [msg, chatbot]).then(
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bot, [chatbot, session_id], [chatbot]
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)
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