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DEV: first trial
Browse files- .gitignore +1 -0
- README.md +2 -0
- app.py +26 -50
- requirements.txt +3 -1
.gitignore
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*.csv
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README.md
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pinned: false
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license: apache-2.0
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short_description: Web Searcher MCP Server
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---
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An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
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pinned: false
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license: apache-2.0
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short_description: Web Searcher MCP Server
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tags:
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- mcp-server-track
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---
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An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
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app.py
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import gradio as gr
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from huggingface_hub import InferenceClient
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"""
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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system_message,
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max_tokens,
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temperature,
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top_p,
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):
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messages = [{"role": "system", "content": system_message}]
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages,
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max_tokens=max_tokens,
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stream=True,
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temperature=temperature,
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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label="Top-p (nucleus sampling)",
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),
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],
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)
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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from langchain_community.tools import DuckDuckGoSearchResults
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from typing import Literal, List, Dict
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def search(input_query : str, max_results : int = 5) -> List[Dict[Literal["snippet", "title", "link"], str]]:
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"""
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Perform a web search using DuckDuckGo.
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Args:
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input_query: The query to search for.
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max_results: The maximum number of results to return. Defaults to 5.
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Returns:
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A list of dictionaries, each containing "snippet", "title", and "link" keys.
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"""
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search = DuckDuckGoSearchResults(output_format="list", num_results = max_results)
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results = search.invoke(input_query)
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return results
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demo = gr.Interface(
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fn = search,
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inputs=[
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gr.Textbox(value="Ahly SC of Egypt matches.", label="Search query"),
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gr.Slider(minimum=1, maximum=20, value=5, step=1, label="Max results"),
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],
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outputs=gr.JSON(label="Search results"),
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title = "Web Searcher using DuckDuckGo",
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description = "Search the web using DuckDuckGo.",
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)
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# Launch the interface and MCP server
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if __name__ == "__main__":
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demo.launch(mcp_server=True)
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requirements.txt
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gradio[mcp]
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duckduckgo-search
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langchain-community
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