new idea implementation

app.py

@@ -4,6 +4,7 @@ import streamlit as st
 from pydantic_ai import Agent,RunContext, Tool
 from pydantic_ai.models.groq import GroqModel
 import nest_asyncio
+from pydantic_ai.messages import ModelMessage
 import pdfplumber
 #from transformers import pipeline
 #import torch
@@ -58,7 +59,6 @@ class SupportDependencies:
 async def ppt_content(data):
     agent = Agent(model,
     result_type=customClass.PPT,
-    deps_type=SupportDependencies,
     tools=[
         return_data,
     ],
@@ -80,18 +80,23 @@ async def ppt_content(data):
     ))
     listOfString = split_into_token_chunks("".join(data))
     print(len(listOfString))
+    message_history: list[ModelMessage]  = []
     # for i, chunk in enumerate(listOfString):
     #     print(f"Chunk {i}:\n{chunk}\n")
 
-    # for x in listOfString:
-    #     result: RunResult[str] = RunResult(_all_messages:[])
-    #     result = agent.run_sync(user_prompt = x,message_history = result.new_messages())
+    for x in listOfString:
+        result = agent.run_sync(user_prompt = f"Create me a powerpoint presentation {x}",message_history = message_history)
+        message_history = result.all_messages()
+        print(result)
 
 
     deps :SupportDependencies = SupportDependencies(db="".join(data))
-    result_1 = agent.run_sync(user_prompt = f"Create me a powerpoint presentation",deps=deps)
-    #print(result_1.data)
-    print(result_1)
+    # while len(listOfString) > 0:
+
+    #     result_1 = agent.run_sync(user_prompt = f"Create me a powerpoint presentation",message_history=message_history)
+    # #print(result_1.data)
+    #     message_history = result_1.all_messages()
+    #     print(result_1)
     
 
 def ai_ppt(data):