Solshine commited on
Commit
302bb07
1 Parent(s): 8049885

Rough draft first pass

Browse files
Files changed (1) hide show
  1. app.py +84 -0
app.py ADDED
@@ -0,0 +1,84 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from langchain.llms import OpenAI
2
+ from langchain.agents import TextProcessingAgent
3
+ from dspy.agents import Agent # Base class for custom agent
4
+ from dspy.utils import spawn_processes # Distributed computing utility
5
+
6
+ # API key **ADD A KEY OR LOCAL LLM PATHWAY**
7
+ openai = OpenAI(api_key="KEY")
8
+
9
+
10
+ # User prompt intake
11
+ user_prompt = "What are the potential strategies to increase my online sales?"
12
+
13
+
14
+ # Synthetic data generation (using Langchain's Text-Davinci-003 model for illustration)
15
+ def generate_synthetic_data(prompt):
16
+ response = openai.complete(prompt=prompt, engine="text-davinci-003", max_tokens=100)
17
+ return response.choices[0].text
18
+
19
+
20
+ # Custom data processing agent (inheriting from DSPy's Agent class) [TONIC PLEASE HELP LOL]
21
+ class DataProcessingAgent(Agent):
22
+ def __init__(self):
23
+ super().__init__()
24
+
25
+ def process(self, data):
26
+ # Implement our custom data processing logic here (e.g., feature engineering)
27
+ processed_data = data.lower().strip()
28
+ return processed_data
29
+
30
+
31
+ # Dynamic team composition (replace with logic for dynamic team creation)
32
+ team = [
33
+ OpenAI(api_key="YOUR_OPENAI_API_KEY", engine="text-davinci-003"), # LLM agent
34
+ DataProcessingAgent(), # Custom data processing agent
35
+ ]
36
+
37
+
38
+ # Prompt and data flow refinement
39
+ combined_data = f"{user_prompt}\n{generate_synthetic_data(f'Simulate scenarios for {user_prompt}')}"
40
+
41
+ # [TONIC PLEASE HELP LOL]
42
+ for agent in team:
43
+ combined_data = agent.process(combined_data)
44
+
45
+
46
+ # Multimedia output production (using Langchain's Text-Davinci-003 as default) because I don't know how to implement DSPy properly yet [TONIC PLEASE HELP LOL]
47
+ def produce_outputs(processed_data):
48
+ # Use Langchain for LLM-based analysis, recommendations, etc. Should this be updated to DSPy too? again:[TONIC PLEASE HELP LOL]
49
+ analysis = openai.complete(prompt=f"Analyze {processed_data}", engine="text-davinci-003", max_tokens=200)
50
+ recommendations = openai.complete(prompt=f"Recommend strategies based on {processed_data}", engine="text-davinci-003", max_tokens=100)
51
+ # Replace with your visualization logic
52
+ visualization = None
53
+ return analysis.choices[0].text, recommendations.choices[0].text, visualization
54
+
55
+
56
+ # Synth data generation using DSPy's distributed computing capabilities (taken partially from DSPY documentation)
57
+ def generate_synthetic_data_distributed(prompt, num_nodes=3):
58
+ # Spawn synthetic data generation processes across multiple nodes
59
+ processes = [spawn_processes(generate_synthetic_data, [f"Simulate scenarios for {prompt}"]) for _ in range(num_nodes)]
60
+
61
+ # Collect the results from each node
62
+ synthetic_data_list = []
63
+ for process in processes:
64
+ synthetic_data_list.extend(process.get())
65
+
66
+ # Combine the results and return the synthetic data
67
+ return "\n".join(synthetic_data_list)
68
+
69
+
70
+ # Generate synthetic data using DSPy's distributed computing capabilities. Again:[TONIC PLEASE HELP LOL]
71
+ synthetic_data = generate_synthetic_data_distributed(user_prompt)
72
+
73
+
74
+ # Generate outputs
75
+ report, recommendations, visualization = produce_outputs(combined_data)
76
+
77
+
78
+ # Print the results
79
+ print("Report:")
80
+ print(report)
81
+ print("\nRecommendations:")
82
+ print(recommendations)
83
+ print("\nVisualization:")
84
+ print(visualization) # Currently "None" due to placeholder 'visualization'