Update app.py

app.py

@@ -1,9 +1,10 @@
 import os
 import requests
+import numpy as np
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load model and tokenizer
+# Load the Qwen model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B")
 model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B")
 
@@ -52,38 +53,30 @@ class Swarm:
     def gather_results(self):
         return [agent.results for agent in self.agents if agent.results]
 
-def generate_tasks_from_model(prompt, num_tasks):
-    # Generate tasks using the Qwen model
+def generate_tasks_from_model(api_url, num_tasks):
+    prompt = f"Generate {num_tasks} API call tasks for the following API URL: {api_url}"
     inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(**inputs, max_length=100, num_return_sequences=num_tasks)
     tasks = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
     return tasks
 
-def run_swarm(prompt, api_key, num_agents, num_tasks):
-    # Generate tasks using the model
-    tasks = generate_tasks_from_model(prompt, num_tasks)
-    
-    # Create a swarm with a fractal pattern (Pentagonal spread)
+def run_swarm(api_url, api_key, num_agents, num_tasks):
+    tasks = generate_tasks_from_model(api_url, num_tasks)
     swarm = Swarm(num_agents=num_agents, fractal_pattern="Pentagonal", api_key=api_key)
     swarm.assign_tasks(tasks)
     swarm.execute()
-
-    # Gather results
     results = swarm.gather_results()
-
-    # Print all results
     print("\nAll results retrieved by the swarm:")
     for i, result in enumerate(results):
         print(f"Result {i + 1}: {result}")
-
     return results
 
-def gradio_interface(prompt, api_key, num_agents, num_tasks):
-    results = run_swarm(prompt, api_key, num_agents, num_tasks)
+def gradio_interface(api_url, api_key, num_agents, num_tasks):
+    results = run_swarm(api_url, api_key, num_agents, num_tasks)
     return "\n".join(str(result) for result in results)
 
 # Default values for the inputs
-default_prompt = "Generate API calls to fetch random cat facts."
+default_api_url = "https://meowfacts.herokuapp.com/"
 default_api_key = ""
 default_num_agents = 5
 default_num_tasks = 2
@@ -91,7 +84,7 @@ default_num_tasks = 2
 iface = gr.Interface(
     fn=gradio_interface,
     inputs=[
-        gr.Textbox(label="Prompt", placeholder="Enter the prompt", value=default_prompt),
+        gr.Textbox(label="API URL", placeholder="Enter the API URL", value=default_api_url),
         gr.Textbox(label="API Key (Optional)", placeholder="Enter the API Key", value=default_api_key),
         gr.Number(label="Number of Agents", value=default_num_agents, precision=0),
         gr.Number(label="Number of Tasks", value=default_num_tasks, precision=0)
@@ -99,13 +92,13 @@ iface = gr.Interface(
     outputs=gr.Textbox(label="Results"),
     title="Swarm Model Processing and Result Gatherer",
     description="""
-        This Gradio app demonstrates a swarm of agents using a language model to generate API calls and gather results.
-        - The language model generates API calls based on the provided prompt.
+        This Gradio app demonstrates a swarm of agents using a language model to generate API call tasks and gather results.
+        - The language model generates API calls based on the provided API URL.
         - The swarm is created based on a fractal geometry pattern.
         - Each agent makes an API call to the generated URLs and retrieves data.
         - The results from all agents are gathered and displayed.
-        - Enter the prompt, API Key (optional), number of agents, and number of tasks to see the process in action.
-        - By default, the app uses the prompt 'Generate API calls to fetch random cat facts' with 5 agents and 2 tasks.
+        - Enter the API URL, API Key (optional), number of agents, and number of tasks to see the process in action.
+        - By default, the app uses the API URL 'https://meowfacts.herokuapp.com/' with 5 agents and 2 tasks.
     """
 )