Upload 2 files

agent.py

@@ -1,3 +1,5 @@
+import tempfile
+from urllib.parse import urlparse
 from langchain.schema import HumanMessage, AIMessage, SystemMessage
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import AnyMessage, SystemMessage
@@ -23,84 +25,208 @@ from langchain_huggingface import (
     HuggingFaceEmbeddings,
 )
 
+from langchain_google_genai import ChatGoogleGenerativeAI
+import requests
+from huggingface_hub import login
 
 load_dotenv()
 
 
-# Initialize the DuckDuckGo search tool
-search_tool = DuckDuckGoSearchResults()
+@tool
+def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
+    """
+    Save content to a temporary file and return the path.
+    Useful for processing files from the GAIA API.
+
+    Args:
+        content: The content to save to the file
+        filename: Optional filename, will generate a random name if not provided
+
+    Returns:
+        Path to the saved file
+    """
+    temp_dir = tempfile.gettempdir()
+    if filename is None:
+        temp_file = tempfile.NamedTemporaryFile(delete=False)
+        filepath = temp_file.name
+    else:
+        filepath = os.path.join(temp_dir, filename)
+
+    # Write content to the file
+    with open(filepath, "w") as f:
+        f.write(content)
+
+    return f"File saved to {filepath}. You can read this file to process its contents."
 
 
 @tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia for a query and return maximum 2 results.
+def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
+    """
+    Download a file from a URL and save it to a temporary location.
 
     Args:
-        query: The search query."""
-    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ]
-    )
-    return {"wiki_results": formatted_search_docs}
+        url: The URL to download from
+        filename: Optional filename, will generate one based on URL if not provided
+
+    Returns:
+        Path to the downloaded file
+    """
+    try:
+        # Parse URL to get filename if not provided
+        if not filename:
+            path = urlparse(url).path
+            filename = os.path.basename(path)
+            if not filename:
+                # Generate a random name if we couldn't extract one
+                import uuid
+
+                filename = f"downloaded_{uuid.uuid4().hex[:8]}"
+
+        # Create temporary file
+        temp_dir = tempfile.gettempdir()
+        filepath = os.path.join(temp_dir, filename)
+
+        # Download the file
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+
+        # Save the file
+        with open(filepath, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+        return f"File downloaded to {filepath}. You can now process this file."
+    except Exception as e:
+        return f"Error downloading file: {str(e)}"
 
 
 @tool
-def web_search(query: str) -> str:
-    """Search Tavily for a query and return maximum 3 results.
+def extract_text_from_image(image_path: str) -> str:
+    """
+    Extract text from an image using pytesseract (if available).
 
     Args:
-        query: The search query."""
-    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ]
-    )
-    return {"web_results": formatted_search_docs}
+        image_path: Path to the image file
+
+    Returns:
+        Extracted text or error message
+    """
+    try:
+        # Try to import pytesseract
+        import pytesseract
+        from PIL import Image
+
+        # Open the image
+        image = Image.open(image_path)
+
+        # Extract text
+        text = pytesseract.image_to_string(image)
+
+        return f"Extracted text from image:\n\n{text}"
+    except ImportError:
+        return "Error: pytesseract is not installed. Please install it with 'pip install pytesseract' and ensure Tesseract OCR is installed on your system."
+    except Exception as e:
+        return f"Error extracting text from image: {str(e)}"
 
 
 @tool
-def arvix_search(query: str) -> str:
-    """Search Arxiv for a query and return maximum 3 result.
+def analyze_csv_file(file_path: str, query: str) -> str:
+    """
+    Analyze a CSV file using pandas and answer a question about it.
 
     Args:
-        query: The search query."""
-    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
-            for doc in search_docs
-        ]
-    )
-    return {"arvix_results": formatted_search_docs}
-
-
-# Load LLM model
-llm = ChatOpenAI(
-    model="gpt-4o",
-    base_url="https://models.inference.ai.azure.com",
-    api_key=os.environ["GITHUB_TOKEN"],
-    temperature=0.2,
-    max_tokens=4096,
-)
+        file_path: Path to the CSV file
+        query: Question about the data
+
+    Returns:
+        Analysis result or error message
+    """
+    try:
+        import pandas as pd
+
+        # Read the CSV file
+        df = pd.read_csv(file_path)
+
+        # Run various analyses based on the query
+        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
+        result += f"Columns: {', '.join(df.columns)}\n\n"
+
+        # Add summary statistics
+        result += "Summary statistics:\n"
+        result += str(df.describe())
+
+        return result
+    except ImportError:
+        return "Error: pandas is not installed. Please install it with 'pip install pandas'."
+    except Exception as e:
+        return f"Error analyzing CSV file: {str(e)}"
+
+
+@tool
+def analyze_excel_file(file_path: str, query: str) -> str:
+    """
+    Analyze an Excel file using pandas and answer a question about it.
+
+    Args:
+        file_path: Path to the Excel file
+        query: Question about the data
+
+    Returns:
+        Analysis result or error message
+    """
+    try:
+        import pandas as pd
+
+        # Read the Excel file
+        df = pd.read_excel(file_path)
+
+        # Run various analyses based on the query
+        result = (
+            f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
+        )
+        result += f"Columns: {', '.join(df.columns)}\n\n"
+
+        # Add summary statistics
+        result += "Summary statistics:\n"
+        result += str(df.describe())
+
+        return result
+    except ImportError:
+        return "Error: pandas and openpyxl are not installed. Please install them with 'pip install pandas openpyxl'."
+    except Exception as e:
+        return f"Error analyzing Excel file: {str(e)}"
+
+
+# Initialize the DuckDuckGo search tool
+search_tool = DuckDuckGoSearchResults()
+
+
+# # Load LLM model
+# llm = ChatOpenAI(
+#     model="gpt-4o",
+#     base_url="https://models.inference.ai.azure.com",
+#     api_key=os.environ["GITHUB_TOKEN"],
+#     temperature=0.2,
+#     max_tokens=4096,
+# )
 # llm = ChatHuggingFace(
 #     llm=HuggingFaceEndpoint(
 #         # repo_id="microsoft/Phi-3-mini-4k-instruct",
-#         repo_id="Qwen/Qwen3-235B-A22B",
+#         repo_id="meta-llama/Llama-3-70B-Instruct",
 #         temperature=0,
 #         # huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_API_TOKEN"],
 #     ),
 #     verbose=True,
 # )
-
+llm = ChatGoogleGenerativeAI(
+    model="gemini-2.0-flash-exp", google_api_key=os.environ["GOOGLE_API_KEY"]
+)
 tools = [
-    arvix_search,
-    wiki_search,
-    # web_search,
+    analyze_csv_file,
+    analyze_excel_file,
+    extract_text_from_image,
+    download_file_from_url,
+    save_and_read_file,
     # search_tool,
 ]
 # Bind the tools to the LLM
@@ -108,30 +234,63 @@ model_with_tools = llm.bind_tools(tools)
 tool_node = ToolNode(tools)
 
 
-def build_agent_workflow():
+class AgentState(TypedDict):
+    """State of the agent."""
 
-    def should_continue(state: MessagesState):
-        messages = state["messages"]
-        last_message = messages[-1]
-        if last_message.tool_calls:
-            return "tools"
-        return END
-
-    def call_model(state: MessagesState):
-        system_message = SystemMessage(
-            content=f"""
-        You are a helpful assistant tasked with answering questions using a set of tools.
-        Now, I will ask you a question. Report your thoughts, and finish your answer with the following template:
-        FINAL ANSWER: [YOUR FINAL ANSWER].
-        YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
-        Your answer should only start with "FINAL ANSWER: ", then follows with the answer. """
-        )
+    input_file: Optional[str]
+    messages: Annotated[list[AnyMessage], add_messages]
 
-        messages = [system_message] + state["messages"]
-        print("Messages to LLM:", messages)
 
-        response = model_with_tools.invoke(messages)
-        return {"messages": [response]}
+def build_agent_workflow():
+    """Build the agent workflow."""
+
+    def call_model(state: AgentState):
+        print("State:", state["messages"])
+        question = state["messages"][-1].content
+        context = f"""
+            You are a helpful assistant tasked with answering questions using a set of tools.
+            """
+        # System message
+        if state.get("input_file"):
+            try:
+                with open(state.get("input_file"), "r") as f:
+                    file_content = f.read()
+                    print("File content:", file_content)
+
+                # Determine file type from extension
+                file_ext = os.path.splitext(state.get("input_file"))[1].lower()
+                context = f"""
+                    Question: {question}
+                    This question has an associated file. Here is the file content:
+                    ```{file_ext}
+                    {file_content}
+                    ```
+                    Analyze the file content above to answer the question."""
+            except Exception as file_e:
+                context = f""" Question: {state["message"]}
+                    This question has an associated file at path: {state.get("input_file")}
+                    However, there was an error reading the file: {file_e}
+                    You can still try to answer the question based on the information provided.
+                    """
+
+        if question.startswith(".") or ".rewsna eht sa" in question:
+            context = f"""
+            This question appears to be in reversed text. Here's the reversed version:
+            {state['message'][::-1]}
+            Now answer the question above. Remember to format your answer exactly as requested.
+            """
+        system_prompt = SystemMessage(
+            f"""{context}
+                When answering, provide ONLY the precise answer requested. 
+                Do not include explanations, steps, reasoning, or additional text.
+                Be direct and specific. GAIA benchmark requires exact matching answers.
+                For example, if asked "What is the capital of France?", respond simply with "Paris".
+                """
+        )
+        return {
+            "messages": [model_with_tools.invoke([system_prompt] + state["messages"])],
+            # "input_file": state["input_file"],
+        }
 
     # Define the state graph
     workflow = StateGraph(MessagesState)
@@ -139,20 +298,18 @@ def build_agent_workflow():
     workflow.add_node("tools", tool_node)
 
     workflow.add_edge(START, "agent")
-    workflow.add_conditional_edges("agent", should_continue, ["tools", END])
+    workflow.add_conditional_edges("agent", tools_condition)
     workflow.add_edge("tools", "agent")
-
     app = workflow.compile()
-
     return app
 
 
-if __name__ == "__main__":
-    question = "Who nominated the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016?"
-    # Build the graph
-    graph = build_agent_workflow()
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()
+# if __name__ == "__main__":
+#     question = "Who nominated the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016?"
+#     # Build the graph
+#     graph = build_agent_workflow()
+#     # Run the graph
+#     messages = [HumanMessage(content=question)]
+#     messages = graph.invoke({"messages": messages, "input_file": None})
+#     for m in messages["messages"]:
+#         m.pretty_print()

app.py

@@ -21,9 +21,9 @@ class BasicAgent:
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
         messages = [HumanMessage(content=question)]
-        messages = self.workflow.invoke({"messages": messages})
+        messages = self.workflow.invoke({"messages": messages, "input_file": None})
         answer = messages["messages"][-1].content
-        return answer[14:]
+        return answer
         # fixed_answer = "This is a default answer."
         # print(f"Agent returning fixed answer: {fixed_answer}")
         # return fixed_answer