code_tool

app.py

@@ -25,11 +25,12 @@ from tools import (
     wikipedia_search_tool,
     ocr_image_tool,
     audio_transcriber_tool,
-    parse_excel_tool
+    parse_excel_tool,
+    analyze_code_tool
 )
 
 # ─────────────────────────── Configuration ───────────────────────────────
-LLM = ChatOpenAI(model_name="gpt-4.1", temperature=0.0)
+LLM = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0.3)
 MAX_TOOL_CALLS = 5
 
 # ─────────────────────────── Helper utilities ────────────────────────────
@@ -92,21 +93,25 @@ def tool_selector(state: AgentState) -> AgentState:
 
     prompt = SystemMessage(
         content=(
+            "if the tool you want isnt listed below, return {'action':'final'} \n"
+            "Use wiki if you need to search online for information. Keep the query short and concise and accurate. The query should not be a prompt but instad you should search for the relevant information rather than asking for the answer directly.\n"
+            "If the question is about any image, you have to use ocr tool. It will tell you about the image also\n"
+            "Use audio if the question is about an audio file\n"
+            "Use excel if the question is about an excel file\n"
+            "Use code if the question is about a code file, or if you want to run your own code\n"
             "Reply with ONE JSON only (no markdown). Choices:\n"
             "  {'action':'wiki','query':'…'}\n"
             "  {'action':'ocr'}\n"
             "  {'action':'audio'}\n"
             "  {'action':'excel'}\n"
+            "  {'action':'code', 'snippet':'<python code>'}\n"
+            "  {'action':'code', 'file':'<name>.py'}\n"
             "  {'action':'final'}\n"
-            "if the tool you want isnt listed above, return {'action':'final'} \n"
-            "Use wiki if you need to search online for information. Keep the query short and concise and accurate. The query should not be a prompt but instad you should search for the relevant information rather than asking for the answer directly.\n"
-            "If the question is about any image, you have to use ocr tool. It will tell you about the image also\n"
-            "Use audio if the question is about an audio file\n"
-            "Use excel if the question is about an excel file\n"
+
             
         )
     )
-    raw = LLM(state.messages + [prompt]).content.strip()
+    raw = LLM.invoke(state.messages + [prompt]).content.strip()
     print(f"Tool selector response: {raw}")
     state.add(AIMessage(content=raw))
     parsed = safe_json(raw)
@@ -157,6 +162,16 @@ def excel_tool(state: AgentState) -> AgentState:
     state.next_action = None
     return state
 
+def code_tool(state: AgentState) -> AgentState:
+    out = {"analysis": analyze_code_tool({
+        "task_id": state.task_id,
+        "snippet": state.snippet,
+        "file": state.file
+    })}
+    state.tool_calls += 1
+    state.add(SystemMessage(content=f"CODE_TOOL_OUT: {brief(out)}"))
+    state.next_action = None
+    return state
 
 # ------------- final answer -------------
 
@@ -167,7 +182,7 @@ def final_node(state: AgentState) -> AgentState:
         "reply **only** with "
         "{\"final_answer\":\"…\"} (no markdown, no commentary)."
     )
-    raw = LLM(state.messages + [wrap]).content.strip()
+    raw = LLM.invoke(state.messages + [wrap]).content.strip()
     # print("raw : ", raw)
     state.add(AIMessage(content=raw))
     parsed = safe_json(raw)
@@ -187,6 +202,7 @@ for name, fn in [
     ("ocr_tool", ocr_tool),
     ("audio_tool", audio_tool),
     ("excel_tool", excel_tool),
+    ("code_tool", code_tool),
     ("final_node", final_node),
 ]:
     graph.add_node(name, fn)
@@ -200,6 +216,7 @@ def dispatch(state: AgentState) -> str:
         "ocr": "ocr_tool",
         "audio": "audio_tool",
         "excel": "excel_tool",
+        "code": "code_tool",
         "final": "final_node",
     }.get(state.next_action, "final_node")
 
@@ -211,12 +228,13 @@ graph.add_conditional_edges(
         "ocr_tool": "ocr_tool",
         "audio_tool": "audio_tool",
         "excel_tool": "excel_tool",
+        "code_tool": "code_tool",
         "final_node": "final_node",
     },
 )
 
 # tools loop back to selector
-for tool_name in ("wiki_tool", "ocr_tool", "audio_tool", "excel_tool"):
+for tool_name in ("wiki_tool", "ocr_tool", "audio_tool", "excel_tool", "code_tool"):
     graph.add_edge(tool_name, "tool_selector")
 
 # final_answer → END

tools.py

@@ -146,6 +146,7 @@ def parse_excel_tool(args: dict) -> str:
             xls,
             sheet_name=sheet if sheet and sheet in xls.sheet_names else xls.sheet_names[0]
         )
+        print(f"Excel file read successfully: {str(df.to_dict(orient='records'))}")
         return str(df.to_dict(orient="records"))
     except Exception as e:
         return f"Error reading Excel file: {e}"
@@ -263,7 +264,44 @@ def wikipedia_search_tool(args: dict) -> str:
         return f"Unexpected error in wikipedia_search_tool: {e}"
 
 
+from langchain_openai import ChatOpenAI
+from langchain.schema import SystemMessage, HumanMessage
+LLM = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0.2)
 
+def analyze_code_tool(args: dict) -> str:
+    """
+    Either args['snippet']  OR  (args['file'] + args['task_id'])
+    Reads the code (max 400 lines / 10 kB) and asks the LLM for:
+        • plain-language summary
+        • list of key functions/classes
+        • obvious bugs or style smells
+    Returns that analysis as a string.
+    """
+    code_txt = ""
+    if args.get("snippet"):
+        code_txt = args["snippet"]
+    elif args.get("file") and args.get("task_id"):
+        path = _download_file_for_task(args["task_id"], "py")
+        if not path:
+            return "Error: .py file not found for this task."
+        code_txt = Path(path).read_text(encoding="utf-8", errors="ignore")
+    else:
+        return "Error: neither snippet nor file provided."
+
+    # Truncate for safety
+    lines = code_txt.splitlines()[:400]
+    code_sample = "\n".join(lines)[:10_000]
+
+    prompt = [
+        SystemMessage(content="You are a senior Python code reviewer."),
+        HumanMessage(content=(
+            "Please analyse the following code. "
+            "Summarise what it does, list key functions/classes, "
+            "and point out any obvious bugs, performance issues or style problems.\n\n"
+            f"```python\n{code_sample}\n```"
+        ))
+    ]
+    return LLM.invoke(prompt).content.strip()
 
 
 # def web_search_tool(state: AgentState) -> AgentState: