Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 from google import genai
 import json
 
@@ -34,12 +33,24 @@ def show_parts(r):
 
 
 client = genai.Client(api_key="AIzaSyD6voSAiSUim17kB90skpdisMMyFXZPxMo")
-MODEL_ID = "gemini-2.0-flash-exp"
+MODEL_ID = "gemini-2.0-flash-thinking-exp"
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
 #client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+import google.generativeai as genai2
+genai2.configure(api_key=os.environ["GOOGLE_API_KEY"]
+model_gen = genai2.GenerativeModel(model_name="gemini-2.0-flash-thinking-exp",
+      generation_config=generation_config,
+      system_instruction=system_instruction,
+      safety_settings=safety_settings)
+
+)
+
+def model_response(text):
+   response = model_gen.generate_content(text)
+   return response.text
 search_tool = {'google_search': {}}
 code_tool = {'code_execution':{}}
 tools = [
@@ -47,65 +58,65 @@ tools = [
     {'code_execution': {}}
 ]
 soccer_chat = client.chats.create(model="gemini-2.0-flash-exp", config={'tools': [search_tool]})
+coder_chat = client.chats.create(model="gemini-2.0-flash-exp", config={'tools': [code_tool]})
+
+def agentville(problem):
+  memory = {}
+  output = ""
+  final_response = ""
+  plan = model_response(f'''You are a thinker. Think long and hard about the problem: {problem} and come up with the steps required to solve the problem. You are supposed to come up with 5 steps to build a solution.''')
+  print ("Plan", plan)
+  output += plan
+  yield output, final_response
+  for i in range(1,6):
+    print ("Step:", i)
+    output += f"Step {i} "
+    yield output, final_response
+    step = model_response(f'''Extract the {i}th step from the given plan: {plan}.  Figure out which of the below agents are required to solve it. 
+    Once you figure out the agent, just output the name of the agent.
+Do not output anything else. The agents at your disposal are:
+Researcher: Has access to Google search tool, can search for resources and answer the questions
+Coder: Expert programmer. Can solve any problem at disposal. Output the name of the agent and nothing else. 
+
+Your response should be in the following format:
+  "Step": <the complete description of the step to be executed>
+  "agent_name": "Researcher/Coder"
+  ''')
+    print ("Current step", step)
+    output += step
+    yield output, final_response
+    if 'Coder' in step:
+      print ("Agent is coder")
+      r = coder_chat.send_message(f'''Complete the step:{step}''')
+      execution_step = show_parts(r)
+      output += execution_step
+      yield output, final_response
+    else:
+      print ("Agent is Researcher")
+      r = soccer_chat.send_message(f'''Complete the step: {step}''')
+      execution_step = show_parts(r)
+      output += execution_step
+      yield output, final_response
+    memory[i] = execution_step  
+  final_response = model_response(f'''Given the problem statement:{problem} and the progress made by agents: {memory}, come up with the final answer. Do not explain what 
+  the agents have done. Focus on getting the final answer.''')
+  print ("Final response", final_response)
+  output += final_response
+  yield output, final_response
+  return output, final_response
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    '''
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-    '''
-    r = soccer_chat.send_message(f'''{messages}''')
-    response = show_parts(r)
-    print (response)
-    yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)"
-        ),
-    ],
+import gradio as gr
+iface = gr.Interface(
+    fn=agentville,
+    #inputs=["text", "text", "text"],
+    inputs = gr.Textbox(label="Problem"),
+    outputs= [gr.Textbox(label="Agents processing"),
+            gr.Markdown(label="Final response")],
+    title="Agentville: Multi-agent autonomous system",
+    description="See multiple agents working in unison to solve complex problems",
+    theme = gr.themes.Ocean()
 )
 
-
+# Launch the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    iface.queue(max_size=20).launch(share=True,debug=True)