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fd7a12e d829858 fd7a12e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | import gradio as gr
from phi.assistant import Assistant
from phi.llm.openai import OpenAIChat
from phi.llm.groq import Groq
from phi.llm.ollama import Ollama
from phi.tools.duckduckgo import DuckDuckGo
# 模型字典
model_choices = {
"OpenAI Model (gpt-3.5-turbo)": {
"class": OpenAIChat,
"params": {
"model": "gpt-3.5-turbo"
}
},
"Groq Model (llama3-70b-8192)": {
"class": Groq,
"params": {
"model": "llama3-70b-8192"
}
},
"Ollama Model (llama3)": {
"class": Ollama,
"params": {
"model": "llama3"
}
}
}
def ai_search(api_key, model_choice, base_url, query):
if model_choice in ["OpenAI Model (gpt-3.5-turbo)", "Groq Model (llama3-70b-8192)"] and not api_key:
return "API key is required."
if model_choice == "OpenAI Model (gpt-3.5-turbo)" and not base_url:
return "Base URL is required for OpenAI Model."
# 动态创建模型实例
model_info = model_choices[model_choice]
llm_class = model_info["class"]
llm_params = model_info["params"]
if model_choice != "Ollama Model (llama3)":
llm_params["api_key"] = api_key
if model_choice == "OpenAI Model (gpt-3.5-turbo)":
llm_params["base_url"] = base_url
llm_instance = llm_class(**llm_params)
# 创建 Assistant 实例
assistant = Assistant(llm=llm_instance, tools=[DuckDuckGo()], show_tool_calls=True)
# 使用 DuckDuckGo 进行搜索
search_results_generator = assistant.run(f"Search: {query}")
if not search_results_generator:
return "No results found."
# 迭代生成器并合并结果
search_results = "".join(list(search_results_generator))
# 打印搜索结果以检查其结构
print(search_results)
# 格式化搜索结果为 Markdown
markdown_results = f"### Search Results for: {query}\n\n"
markdown_results += "Here are some results:\n\n"
markdown_results += f"{search_results}\n"
return markdown_results
def create_interface():
with gr.Blocks() as iface:
gr.Markdown("## AI Search Engine")
model_choice_input = gr.Dropdown(label="Choose Model", choices=list(model_choices.keys()))
api_key_input = gr.Textbox(label="API Key", placeholder="Enter your API key here", type="password", visible=False)
base_url_input = gr.Textbox(label="Base URL", placeholder="Enter the base URL here", visible=False)
query_input = gr.Textbox(label="Search Query", placeholder="Enter your search query here")
search_button = gr.Button("Search", interactive=False)
output = gr.Textbox(label="Response", interactive=False)
def update_inputs(model_choice):
if model_choice == "OpenAI Model (gpt-3.5-turbo)":
return gr.update(visible=True), gr.update(visible=True), "", ""
elif model_choice == "Groq Model (llama3-70b-8192)":
return gr.update(visible=True), gr.update(visible=False), "", ""
else:
return gr.update(visible=False), gr.update(visible=False), "", ""
def check_button_state(api_key, base_url, model_choice, query):
# print(api_key)
# print(base_url)
# print(model_choice)
# if model_choice == "Ollama Model (llama3)":
# return gr.update(interactive=bool(query))
# elif model_choice == "Groq Model (llama3-70b-8192)":
# return gr.update(interactive=True)
# elif model_choice == "OpenAI Model (gpt-3.5-turbo)":
# return gr.update(interactive=bool(api_key and base_url and query))
# else:
# return gr.update(interactive=True)
return gr.update(interactive=True)
def combined_update(model_choice, api_key, base_url, query):
updates = update_inputs(model_choice)
button_state = check_button_state(api_key, base_url, model_choice, query)
return updates + (button_state,)
model_choice_input.change(
lambda model_choice: combined_update(model_choice, api_key_input.value, base_url_input.value, query_input.value),
inputs=model_choice_input,
outputs=[api_key_input, base_url_input, api_key_input, base_url_input, search_button]
)
api_key_input.change(
lambda api_key: check_button_state(api_key, base_url_input.value, model_choice_input.value, query_input.value),
inputs=api_key_input,
outputs=search_button
)
base_url_input.change(
lambda base_url: check_button_state(api_key_input.value, base_url, model_choice_input.value, query_input.value),
inputs=base_url_input,
outputs=search_button
)
query_input.change(
lambda query: check_button_state(api_key_input.value, base_url_input.value, model_choice_input.value, query),
inputs=query_input,
outputs=search_button
)
search_button.click(
fn=ai_search,
inputs=[api_key_input, model_choice_input, base_url_input, query_input],
outputs=output
)
return iface
iface = create_interface()
iface.launch() |