File size: 4,013 Bytes
63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 63c7e91 2c81105 |
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 |
import os
import gradio as gr
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough, chain
def create_dynamic_chain(api_key):
llm = ChatOpenAI(model="gpt-4o-mini", api_key=api_key)
# Chain for general questions
general_prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant that provides direct answers."),
("human", "{question}")
])
# Chain for mathematical calculations
math_prompt = ChatPromptTemplate.from_messages([
("system", "You are a mathematical assistant. Solve the problem and show your work."),
("human", "{question}")
])
# Chain for coding questions
code_prompt = ChatPromptTemplate.from_messages([
("system", "You are a coding assistant. Provide code examples and explanations."),
("human", "{question}")
])
general_chain = general_prompt | llm | StrOutputParser()
math_chain = math_prompt | llm | StrOutputParser()
code_chain = code_prompt | llm | StrOutputParser()
@chain
def dynamic_chain(input_dict):
question = input_dict["question"].lower()
# Detect question type
if any(word in question for word in ["calculate", "solve", "compute", "sum", "multiply"]):
return math_chain
elif any(word in question for word in ["code", "program", "function", "python", "javascript"]):
return code_chain
return general_chain
return dynamic_chain
def process_message(message, history, api_key, example_select):
if not api_key:
return "", [{"role": "assistant", "content": "Please enter your OpenAI API key."}]
try:
# Handle example selection
if example_select != "Custom Input":
message = EXAMPLES[example_select]
chain = create_dynamic_chain(api_key)
response = chain.invoke({"question": message})
history.append({"role": "user", "content": message})
history.append({"role": "assistant", "content": response})
return "", history
except Exception as e:
return "", history + [{"role": "assistant", "content": f"Error: {str(e)}"}]
# Example questions for different chain types
EXAMPLES = {
"General Question": "What are the main features of renewable energy?",
"Math Problem": "Calculate the area of a circle with radius 5 units.",
"Coding Question": "Write a Python function to find the factorial of a number.",
"Custom Input": ""
}
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# Dynamic Chain Demo with Examples")
with gr.Row():
api_key = gr.Textbox(
label="OpenAI API Key",
placeholder="Enter your OpenAI API key",
type="password"
)
example_select = gr.Dropdown(
choices=list(EXAMPLES.keys()),
value="Custom Input",
label="Select Example"
)
chatbot = gr.Chatbot(type="messages")
msg = gr.Textbox(label="Message", placeholder="Type your message or select an example above")
clear = gr.ClearButton([msg, chatbot])
# Example descriptions
gr.Markdown("""
## Example Types:
1. **General Questions**: Regular queries that don't require special processing
2. **Math Problems**: Questions involving calculations and mathematical operations
3. **Coding Questions**: Programming-related queries that return code examples
## Try these patterns:
- Math: "Calculate...", "Solve...", "Compute..."
- Code: "Write a function...", "Program...", "Code..."
- General: Any other type of question
""")
msg.submit(
process_message,
inputs=[msg, chatbot, api_key, example_select],
outputs=[msg, chatbot]
)
if __name__ == "__main__":
demo.launch() |