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Update app.py
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app.py
CHANGED
@@ -16,6 +16,7 @@ from tavily import TavilyClient
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import requests
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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# Initialize models and clients
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MODEL = 'llama3-groq-70b-8192-tool-use-preview'
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@@ -48,18 +49,9 @@ def play_voice_output(response):
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# NumPy Code Calculator Tool
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def numpy_code_calculator(query):
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try:
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model=MODEL,
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messages=[
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{"role": "user", "content": f"Write NumPy code to: {query}"}
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]
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)
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code = llm_response.choices[0].message.content
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print(f"Generated NumPy code:\n{code}")
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# Execute the code in a safe environment
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local_dict = {"np": np}
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exec(
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result = local_dict.get("result", "No result found")
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return str(result)
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except Exception as e:
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@@ -82,8 +74,6 @@ def image_generation(query):
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return "output.jpg"
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# Function to handle different input types and choose the right tool
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from llama_index.core.chat_engine.types import AgentChatResponse
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def handle_input(user_prompt, image=None, audio=None, websearch=False):
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if audio:
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if isinstance(audio, str):
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@@ -115,7 +105,7 @@ def handle_input(user_prompt, image=None, audio=None, websearch=False):
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# Extract the content from AgentChatResponse to return as a string
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if isinstance(response, AgentChatResponse):
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response = response.
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return response
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@@ -188,4 +178,4 @@ def main_interface(user_prompt, image=None, audio=None, voice_only=False, websea
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# Launch the UI
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demo = create_ui()
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demo.launch()
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import requests
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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from llama_index.core.chat_engine.types import AgentChatResponse
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# Initialize models and clients
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MODEL = 'llama3-groq-70b-8192-tool-use-preview'
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# NumPy Code Calculator Tool
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def numpy_code_calculator(query):
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try:
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# Assume query is a request for a numpy computation
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local_dict = {"np": np}
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exec(query, local_dict)
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result = local_dict.get("result", "No result found")
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return str(result)
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except Exception as e:
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return "output.jpg"
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# Function to handle different input types and choose the right tool
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def handle_input(user_prompt, image=None, audio=None, websearch=False):
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if audio:
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if isinstance(audio, str):
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# Extract the content from AgentChatResponse to return as a string
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if isinstance(response, AgentChatResponse):
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response = response.response
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return response
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# Launch the UI
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demo = create_ui()
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demo.launch()
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