update

agent.py

@@ -12,8 +12,11 @@ from langgraph.prebuilt import ToolNode
 from typing_extensions import TypedDict, Annotated
 
 
+
 class State(TypedDict):
     messages: Annotated[list, add_messages]
+    content_type: str
+    content: str
     aggregate: Annotated[list, operator.add]
     # graph_state: str
 
@@ -73,12 +76,16 @@ def get_graph(llm):
 
         return serialized
 
+
+
     tools = [retrieve, online_search]
     tool_node = ToolNode(tools)
     llm_with_tools = llm.bind_tools(tools)
 
     def make_plan(state: State):
+
         print("\n-------------------- Starting to create a plan --------------------\n")
+        print("Content is: ", state["content_type"])
         # get all messages from the state
         messages = state["messages"]
         # append planning message
@@ -103,10 +110,7 @@ def get_graph(llm):
         prompt_answer = prompt_template.invoke(messages)
         # invoke LLM
         response = llm_with_tools.invoke(prompt_answer)
-        print("\nThe Prompt is: ", prompt_answer, "\n")
-        print("Agent has made a decision:\n",response, response.content, response.tool_calls)
-        print("Type von der Antwort: ",type(response))
-
+        print("Agent has made a decision:\n", response.content, response.tool_calls)
         print("Waiting for 4 seconds...")
         time.sleep(4)
 
@@ -121,6 +125,7 @@ def get_graph(llm):
         prompt_answer = prompt_template.invoke(messages)
         # invoke LLM
         response = llm.invoke(prompt_answer)
+        print("The final answer is: ", response.content)
         return {"messages": [response], "aggregate": ["Answer"]}
 
     def should_continue(state: State):

app.py

@@ -6,8 +6,10 @@ import pandas as pd
 from dotenv import load_dotenv
 import json
 import time
+import base64
 
 from agent import *
+from app_playground import response
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -22,9 +24,30 @@ class BasicAgent:
         self.graph = get_graph(self.llm)
 
         print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
+    def __call__(self, question: str, content=None, content_type= None) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        response = self.graph.invoke({"messages": [HumanMessage(content=question),]})
+        if content_type == "image/png":
+            image = base64.b64encode(content).decode("utf-8")
+
+            message = {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "What do you see in the image?",
+                    },
+                    {
+                        "type": "image",
+                        "source_type": "base64",
+                        "data": image,
+                        "mime_type": "image/png",
+                    },
+                ],
+            }
+            response = self.graph.invoke(message)
+        else :
+            response = self.graph.invoke({"messages": [HumanMessage(content=question),]})
+
         print("Agents answert to the question: ",response["messages"][-1].content)
         return response["messages"][-1].content
 
@@ -91,11 +114,22 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         time.sleep(10)
         task_id = item.get("task_id")
         question_text = item.get("question")
+
+        # Check if there is a additional file to be used
+        if item.get("file_name"):
+            file = requests.get(f"{api_url}/files/{task_id}")
+            content_type = file.headers.get("Content-Type")
+
+        if content_type ==  "image/png":
+            content = file.content
+
+
+
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            submitted_answer = agent(question_text, content, content_type)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:

app_playground.ipynb

@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-04-27T21:10:45.275032Z",
-     "start_time": "2025-04-27T21:10:39.005442Z"
+     "end_time": "2025-04-28T11:49:33.800394Z",
+     "start_time": "2025-04-28T11:49:27.768470Z"
     }
    },
    "source": [
@@ -28,8 +28,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-27T21:10:45.374522Z",
-     "start_time": "2025-04-27T21:10:45.281121Z"
+     "end_time": "2025-04-28T11:24:12.828295Z",
+     "start_time": "2025-04-28T11:24:01.696627Z"
     }
    },
    "cell_type": "code",
@@ -454,13 +454,588 @@
    ],
    "execution_count": 8
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T09:59:31.346154Z",
+     "start_time": "2025-04-28T09:59:26.018247Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import requests\n",
+    "url = \"https://agents-course-unit4-scoring.hf.space\"\n",
+    "task_id = \"cca530fc-4052-43b2-b130-b30968d8aa44\"\n",
+    "\n",
+    "\n",
+    "#res = graph.invoke({\"messages\": [HumanMessage(content=\"How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.\"),]})\n",
+    "\n",
+    "res = graph.invoke({\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\"type\": \"text\", \"text\": \"Review the chess position provided in the image. It is black's turn. Provide the correct next move for black which guarantees a win. Please provide your response in algebraic notation.\"},\n",
+    "        {\"type\": \"image\", \"source_type\": \"url\", \"url\": \"https://agents-course-unit4-scoring.hf.space/files/7bd855d8-463d-4ed5-93ca-5fe35145f733\"},\n",
+    "    ],\n",
+    "})\n",
+    "\n"
+   ],
+   "id": "5d4dada891453bed",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-------------------- Starting to create a plan --------------------\n",
+      "\n",
+      "The plan is:  I am unable to assist with that request.\n",
+      "\n",
+      "-------------------- Agent has been called -----------------------------------\n",
+      "\n",
+      "Agent has made a decision:\n",
+      " I am unable to assist with that request. []\n",
+      "Waiting for 4 seconds...\n",
+      "\n",
+      "-------------------- Decision of forwarding has been made --------------------\n",
+      "\n",
+      "This is round:  2\n",
+      "The last message is:  content='I am unable to assist with that request.' additional_kwargs={} response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []} id='run-ebfd867d-672e-4351-b60d-d4a692d44d10-0' usage_metadata={'input_tokens': 269, 'output_tokens': 10, 'total_tokens': 279, 'input_token_details': {'cache_read': 0}}\n",
+      "The final answer is:  I am unable to assist with that request.\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T10:01:39.769344Z",
+     "start_time": "2025-04-28T10:01:39.704253Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the weather in this image:\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"type\": \"image\",\n",
+    "            \"source_type\": \"url\",\n",
+    "            \"url\": image_url,\n",
+    "        },\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
+   ],
+   "id": "9d8999f9f946b596",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Invalid input type <class 'dict'>. Must be a PromptValue, str, or list of BaseMessages.",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[9]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mllm\u001B[49m\u001B[43m.\u001B[49m\u001B[43minvoke\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\n\u001B[32m      2\u001B[39m \u001B[43m    \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mrole\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43muser\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m      3\u001B[39m \u001B[43m    \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mcontent\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mHello!\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m      4\u001B[39m \u001B[43m}\u001B[49m\u001B[43m)\u001B[49m.content\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\Final_Assignment\\.venv\\Lib\\site-packages\\langchain_google_genai\\chat_models.py:1175\u001B[39m, in \u001B[36mChatGoogleGenerativeAI.invoke\u001B[39m\u001B[34m(self, input, config, code_execution, stop, **kwargs)\u001B[39m\n\u001B[32m   1170\u001B[39m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   1171\u001B[39m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m   1172\u001B[39m             \u001B[33m\"\u001B[39m\u001B[33mTools are already defined.\u001B[39m\u001B[33m\"\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mcode_execution tool can\u001B[39m\u001B[33m'\u001B[39m\u001B[33mt be defined\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   1173\u001B[39m         )\n\u001B[32m-> \u001B[39m\u001B[32m1175\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43minvoke\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconfig\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m=\u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\Final_Assignment\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:370\u001B[39m, in \u001B[36mBaseChatModel.invoke\u001B[39m\u001B[34m(self, input, config, stop, **kwargs)\u001B[39m\n\u001B[32m    357\u001B[39m \u001B[38;5;129m@override\u001B[39m\n\u001B[32m    358\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34minvoke\u001B[39m(\n\u001B[32m    359\u001B[39m     \u001B[38;5;28mself\u001B[39m,\n\u001B[32m   (...)\u001B[39m\u001B[32m    364\u001B[39m     **kwargs: Any,\n\u001B[32m    365\u001B[39m ) -> BaseMessage:\n\u001B[32m    366\u001B[39m     config = ensure_config(config)\n\u001B[32m    367\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m cast(\n\u001B[32m    368\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mChatGeneration\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    369\u001B[39m         \u001B[38;5;28mself\u001B[39m.generate_prompt(\n\u001B[32m--> \u001B[39m\u001B[32m370\u001B[39m             [\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_convert_input\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m)\u001B[49m],\n\u001B[32m    371\u001B[39m             stop=stop,\n\u001B[32m    372\u001B[39m             callbacks=config.get(\u001B[33m\"\u001B[39m\u001B[33mcallbacks\u001B[39m\u001B[33m\"\u001B[39m),\n\u001B[32m    373\u001B[39m             tags=config.get(\u001B[33m\"\u001B[39m\u001B[33mtags\u001B[39m\u001B[33m\"\u001B[39m),\n\u001B[32m    374\u001B[39m             metadata=config.get(\u001B[33m\"\u001B[39m\u001B[33mmetadata\u001B[39m\u001B[33m\"\u001B[39m),\n\u001B[32m    375\u001B[39m             run_name=config.get(\u001B[33m\"\u001B[39m\u001B[33mrun_name\u001B[39m\u001B[33m\"\u001B[39m),\n\u001B[32m    376\u001B[39m             run_id=config.pop(\u001B[33m\"\u001B[39m\u001B[33mrun_id\u001B[39m\u001B[33m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m),\n\u001B[32m    377\u001B[39m             **kwargs,\n\u001B[32m    378\u001B[39m         ).generations[\u001B[32m0\u001B[39m][\u001B[32m0\u001B[39m],\n\u001B[32m    379\u001B[39m     ).message\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\Final_Assignment\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:355\u001B[39m, in \u001B[36mBaseChatModel._convert_input\u001B[39m\u001B[34m(self, input)\u001B[39m\n\u001B[32m    350\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m ChatPromptValue(messages=convert_to_messages(\u001B[38;5;28minput\u001B[39m))\n\u001B[32m    351\u001B[39m msg = (\n\u001B[32m    352\u001B[39m     \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mInvalid input type \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtype\u001B[39m(\u001B[38;5;28minput\u001B[39m)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m. \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    353\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33mMust be a PromptValue, str, or list of BaseMessages.\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    354\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m355\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(msg)\n",
+      "\u001B[31mValueError\u001B[39m: Invalid input type <class 'dict'>. Must be a PromptValue, str, or list of BaseMessages."
+     ]
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T11:08:10.375646Z",
+     "start_time": "2025-04-28T11:07:56.818924Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import base64\n",
+    "import io\n",
+    "from PIL import Image\n",
+    "\n",
+    "url = \"https://agents-course-unit4-scoring.hf.space\"\n",
+    "task_id = \"cca530fc-4052-43b2-b130-b30968d8aa44\"\n",
+    "\n",
+    "file = requests.get(f\"{url}/files/{task_id}\").content\n",
+    "\n",
+    "# Load the image into a format that Pillow can handle\n",
+    "#png = Image.open(io.BytesIO(file))\n",
+    "\n",
+    "\n",
+    "image_data = base64.b64encode(file).decode(\"utf-8\")\n",
+    "\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"What do you see in the image?\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"type\": \"image\",\n",
+    "            \"source_type\": \"base64\",\n",
+    "            \"data\": image_data,\n",
+    "            \"mime_type\": \"image/png\",\n",
+    "        },\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
+   ],
+   "id": "348a6be5265c4552",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image shows a chessboard in a complex position.\n",
+      "\n",
+      "*   **White pieces:** King on a1, pawns on a3, b2, c2, Queen on a5, Rook on e3, Bishops on d3 and c3, Queen on b3, pawns on a3.\n",
+      "*   **Black pieces:** King on g8, Rook on d8, Knight on d4, Bishop on e6, pawns on a6, b7, c7, g7, h7.\n",
+      "\n",
+      "It appears to be mid-game or late mid-game. White has a strong presence in the center of the board, while Black's pieces are somewhat restricted.\n"
+     ]
+    }
+   ],
+   "execution_count": 35
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T10:11:38.429058Z",
+     "start_time": "2025-04-28T10:11:38.421435Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "type(file)",
+   "id": "6d31a64d00bde967",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bytes"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T10:21:48.824755Z",
+     "start_time": "2025-04-28T10:21:48.452463Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import base64\n",
+    "import io\n",
+    "from PIL import Image\n",
+    "\n",
+    "url = \"https://agents-course-unit4-scoring.hf.space\"\n",
+    "task_id = \"cca530fc-4052-43b2-b130-b30968d8aa44\"\n",
+    "\n",
+    "file = requests.get(f\"{url}/files/{task_id}\").content\n",
+    "\n",
+    "# Load the image into a format that Pillow can handle\n",
+    "png = Image.open(io.BytesIO(file))"
+   ],
+   "id": "853c311e341ef18a",
+   "outputs": [],
+   "execution_count": 20
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T10:24:13.977190Z",
+     "start_time": "2025-04-28T10:24:13.968578Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "type(file)",
+   "id": "aebc5d4de6ae080",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bytes"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T10:37:59.657656Z",
+     "start_time": "2025-04-28T10:37:48.309745Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "url = \"https://agents-course-unit4-scoring.hf.space\"\n",
+    "task_id = \"3f57289b-8c60-48be-bd80-01f8099ca449\"\n",
+    "\n",
+    "response =requests.get(f\"{url}/files/{task_id}\")"
+   ],
+   "id": "d5fe42a61ec933a7",
+   "outputs": [],
+   "execution_count": 31
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T10:38:02.736542Z",
+     "start_time": "2025-04-28T10:38:02.726023Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "response.headers",
+   "id": "ae31e0a04b38d72e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Date': 'Mon, 28 Apr 2025 10:38:01 GMT', 'Content-Type': 'application/json', 'Content-Length': '87', 'Connection': 'keep-alive', 'server': 'uvicorn', 'x-proxied-host': 'http://10.108.141.57', 'x-proxied-replica': 'hcbpd5es-mboj7', 'x-proxied-path': '/files/3f57289b-8c60-48be-bd80-01f8099ca449', 'link': '<https://huggingface.co/spaces/agents-course/Unit4_scoring>;rel=\"canonical\"', 'x-request-id': 'ptt6H3', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-credentials': 'true'}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 32
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T10:38:27.260636Z",
+     "start_time": "2025-04-28T10:38:27.251750Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "response.content",
+   "id": "cae7cdd9c6a5954c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'{\"detail\":\"No file path associated with task_id 3f57289b-8c60-48be-bd80-01f8099ca449.\"}'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 33
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# Graph Test with Image",
+   "id": "394eb37ceb5347b4"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "1686ea153ccee21"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "bd9f07c22f74d34b"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T11:24:13.797491Z",
+     "start_time": "2025-04-28T11:24:12.841550Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import requests\n",
+    "import base64\n",
+    "\n",
+    "\n",
+    "url = \"https://agents-course-unit4-scoring.hf.space\"\n",
+    "task_id = \"cca530fc-4052-43b2-b130-b30968d8aa44\"\n",
+    "\n",
+    "file = requests.get(f\"{url}/files/{task_id}\")\n",
+    "\n",
+    "content = file.content\n",
+    "\n",
+    "\n",
+    "image = base64.b64encode(content).decode(\"utf-8\")\n",
+    "\n",
+    "message = {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [\n",
+    "            {\n",
+    "                \"type\": \"text\",\n",
+    "                \"text\": \"What do you see in the image?\",\n",
+    "            },\n",
+    "            {\n",
+    "                \"type\": \"image\",\n",
+    "                \"source_type\": \"base64\",\n",
+    "                \"data\": image,\n",
+    "                \"mime_type\": \"image/png\",\n",
+    "            },\n",
+    "            ],\n",
+    "        }\n",
+    "response = graph.invoke({\n",
+    "  \"messages\": [HumanMessage(content=\"What do you see in the image?\")], \"content_type\": \"image/png\", \"content\": image\n",
+    "})\n",
+    "\n",
+    "\n"
+   ],
+   "id": "d6f8c031722bebde",
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'requests' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[3]\u001B[39m\u001B[32m, line 6\u001B[39m\n\u001B[32m      3\u001B[39m url = \u001B[33m\"\u001B[39m\u001B[33mhttps://agents-course-unit4-scoring.hf.space\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m      4\u001B[39m task_id = \u001B[33m\"\u001B[39m\u001B[33mcca530fc-4052-43b2-b130-b30968d8aa44\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m file = \u001B[43mrequests\u001B[49m.get(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00murl\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m/files/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtask_id\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m      8\u001B[39m content = file.content\n\u001B[32m     11\u001B[39m image = base64.b64encode(content).decode(\u001B[33m\"\u001B[39m\u001B[33mutf-8\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[31mNameError\u001B[39m: name 'requests' is not defined"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T11:27:18.431653Z",
+     "start_time": "2025-04-28T11:27:07.008526Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import requests\n",
+    "import base64\n",
+    "\n",
+    "url = \"https://agents-course-unit4-scoring.hf.space\"\n",
+    "task_id = \"cca530fc-4052-43b2-b130-b30968d8aa44\"\n",
+    "\n",
+    "file = requests.get(f\"{url}/files/{task_id}\")\n",
+    "\n",
+    "content = file.content\n",
+    "\n",
+    "\n",
+    "image = base64.b64encode(content).decode(\"utf-8\")"
+   ],
+   "id": "8dfdded8b08191f9",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T11:27:18.458424Z",
+     "start_time": "2025-04-28T11:27:18.446544Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "type(image)",
+   "id": "aff8123098008c84",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T14:56:48.036657Z",
+     "start_time": "2025-04-28T14:56:41.876114Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "from gradio.server_messages import BaseMessage\n",
+    "\n",
+    "from agent import *\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "\n",
+    "llm = get_llm()\n",
+    "\n",
+    "graph = get_graph(llm)\n"
+   ],
+   "id": "9cfbff34b43717aa",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T14:57:56.787741Z",
+     "start_time": "2025-04-28T14:57:48.550907Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import requests\n",
+    "import base64\n",
+    "\n",
+    "\n",
+    "url = \"https://agents-course-unit4-scoring.hf.space\"\n",
+    "task_id = \"cca530fc-4052-43b2-b130-b30968d8aa44\"\n",
+    "\n",
+    "file = requests.get(f\"{url}/files/{task_id}\")\n",
+    "\n",
+    "content = file.content\n",
+    "\n",
+    "\n",
+    "image = base64.b64encode(content).decode(\"utf-8\")\n",
+    "\n",
+    "response = graph.invoke({\n",
+    "  \"messages\": HumanMessage(content=[\n",
+    "        {\"type\": \"text\", \"text\": \"Review the chess position provided in the image. It is black's turn. Provide the correct next move for black which guarantees a win. Please provide your response in algebraic notation.\"},\n",
+    "        {\n",
+    "            \"type\": \"image\",\n",
+    "            \"source_type\": \"base64\",\n",
+    "            \"data\": image,\n",
+    "            \"mime_type\": \"image/png\",\n",
+    "        }\n",
+    "    ]), \"content_type\": \"image/png\", \"content\": image\n",
+    "})\n",
+    "\n",
+    "\n"
+   ],
+   "id": "26bf0c6bb52a851",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-------------------- Starting to create a plan --------------------\n",
+      "\n",
+      "Content is:  image/png\n",
+      "The plan is:  1. Analyze the board state.\n",
+      "2. Identify tactical motifs.\n",
+      "3. Consider forcing moves.\n",
+      "4. Evaluate consequences of each move.\n",
+      "5. Choose the move that leads to a forced win.\n",
+      "\n",
+      "-------------------- Agent has been called -----------------------------------\n",
+      "\n",
+      "Agent has made a decision:\n",
+      " Qxg3+ []\n",
+      "Waiting for 4 seconds...\n",
+      "\n",
+      "-------------------- Decision of forwarding has been made --------------------\n",
+      "\n",
+      "This is round:  2\n",
+      "The last message is:  content='Qxg3+' additional_kwargs={} response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []} id='run-a30f4635-7888-44fa-bdf8-140d2fc7da69-0' usage_metadata={'input_tokens': 1629, 'output_tokens': 5, 'total_tokens': 1634, 'input_token_details': {'cache_read': 0}}\n",
+      "The final answer is:  Qxg3+\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T11:53:52.514018Z",
+     "start_time": "2025-04-28T11:53:50.971863Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "msg = {\n",
+    "                \"role\": \"user\",\n",
+    "                \"content\": [{\n",
+    "                    \"type\": \"text\",\n",
+    "                    \"text\": \"What do you see in the image?\",\n",
+    "                },\n",
+    "                {\n",
+    "                    \"type\": \"image\",\n",
+    "                    \"source_type\": \"base64\",\n",
+    "                    \"data\": image,\n",
+    "                    \"mime_type\": \"image/png\",\n",
+    "\n",
+    "                }]\n",
+    "            }\n",
+    "description = llm.invoke([msg]).content\n"
+   ],
+   "id": "a50a4f682c5efba3",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-28T11:53:55.296323Z",
+     "start_time": "2025-04-28T11:53:55.285807Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "description",
+   "id": "cba853842235c686",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The image shows a chessboard in a middle-game position. White has a king on a1, pawns on a3, b2, and c2, a queen on a5, a rook on e3, bishops on e3 and c3, a queen on b3, and a pawn on a3. Black has a king on g8, pawns on b7, c7, g7, and h7, a bishop on e6, a knight on e4, and a rook on d8.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
+  },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "5d4dada891453bed"
+   "id": "741f44bcb86cd88e"
   }
  ],
  "metadata": {

requirements.txt

@@ -11,4 +11,6 @@ langchain-groq~=0.3.2
 langchain-community ~=0.3.22
 wikipedia ~= 1.4.0
 langchain-google-genai ~=2.1.3
-tavily-python ~=0.7.0
+tavily-python ~=0.7.0
+pandas ~=2.2.3
+matplotlib ~=3.10.1