added corrected code

app.py

@@ -1,53 +1,114 @@
-# app.py
-import gradio as gr
+# pip install langgraph langchain-google-genai langchain-groq
+from langgraph.graph import StateGraph, END
+from typing import TypedDict
 import requests
+from langchain_google_genai import ChatGoogleGenerativeAI # or from langchain_groq import ChatGroQ
 
-from transformers import HfAgent
+# 1. Define your model (Choose one)
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", google_api_key="AIzaSyBkAb7nRruE-Sn-Ff9bMWhiVBvY751pqOk")
+# llm = ChatGroQ(model="mixtral-8x7b-32768", groq_api_key="your_key") # Fast & cheap for experimentation
 
-agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
+# 2. Define your agent's state. What information does it need to carry between steps?
+class AgentState(TypedDict):
+    question: str
+    task_id: str | None  # For fetching files
+    agent_response: str
+    final_answer: str
 
-API_BASE = "https://gaia-course-api.huggingface.co"
+# 3. Define the nodes (functions) of your graph
 
-def get_questions():
-    return requests.get(f"{API_BASE}/questions").json()
+def fetch_question_node(state: AgentState):
+    """Node 1: Calls the /random-question API to get a problem."""
+    response = requests.get("https://agents-course-unit4-scoring.hf.space/random-question")
+    response.raise_for_status()
+    data = response.json()
+    print("this is a data",data)
+    return {
+        "question": data["question"],
+        "task_id": data.get("task_id")  # If the question requires a file, the API might provide an ID
+    }
 
-def run_agent_on_all_questions():
-    questions = get_questions()
-    results = []
+def download_file_node(state: AgentState):
+    """Node 2: If a task_id is present, download the related file."""
+    if not state['task_id']:
+        return {"agent_response": "No file to download."}
     
-    for q in questions:
-        task_id = q["id"]
-        prompt = q["input"]
-        print(f"Running on: {prompt}")
-        try:
-            answer = agent.run(prompt)
-            print(f"Answer: {answer}")
-            results.append({"task_id": task_id, "submitted_answer": answer.strip()})
-        except Exception as e:
-            print("Error:", e)
-            results.append({"task_id": task_id, "submitted_answer": "ERROR"})
-
-    return results
-
-def submit_answers(username, code_link):
-    answers = run_agent_on_all_questions()
-    response = requests.post(
-        f"{API_BASE}/submit",
-        json={
-            "username": username,
-            "agent_code": code_link,
-            "answers": answers
-        },
+    file_url = f"https://agents-course-unit4-scoring.hf.space/files/{state['task_id']}"
+    file_response = requests.get(file_url)
+    # Process the file (e.g., if it's a text file, read it; if it's an image, use a vision model)
+    file_content = file_response.text
+    enhanced_query = f"Question: {state['question']}\n\nUse the following file content to answer:\n{file_content}"
+    return {"agent_response": enhanced_query}
+
+def reason_and_answer_node(state: AgentState):
+    """Node 3: The core reasoning node. Use the LLM to solve the problem."""
+    # Construct a powerful prompt to encourage step-by-step thinking internally,
+    # but demand a final, clean answer.
+    system_prompt = """You are a highly intelligent AI agent solving a question.
+    Think carefully about the problem. Your final output must be ONLY the final answer, with no additional text, explanations, or formatting.
+    """
+    
+    prompt = f"{system_prompt}\n\nQuestion: {state['agent_response']}"
+    response = llm.invoke(prompt)
+    
+    # Here you might need to parse the response to extract *only* the final answer.
+    # For a well-instructed model, `response.content` should be just the answer.
+    return {"final_answer": response.content.strip()}
+
+# def submit_answer_node(state: AgentState):
+#     """Node 4: Submit the final answer to the scoring API."""
+#     payload = {
+#         "question_id": state.get("question_id"), # You might need to get this from the initial fetch
+#         "answer": state['final_answer']
+#     }
+#     submission_response = requests.post("https://agents-course-unit4-scoring.hf.space/submit", json=payload)
+#     print(f"Submission Status: {submission_response.status_code}")
+#     print(f"Submission Response: {submission_response.json()}")
+#     return state
+
+
+
+
+def submit_answer_node(state: AgentState):
+    """Node 4: Submit the final answer to the scoring API."""
+    
+    payload = {
+        "username": "your_hf_username",  # 👈 replace with your Hugging Face username
+        "agent_code": "https://huggingface.co/spaces/your-username/your-space-name/tree/main",  # 👈 replace with your Space link
+        "answers": [
+            {
+                "task_id": state.get("task_id"),  # use task_id from initial fetch
+                "submitted_answer": state["final_answer"]
+            }
+        ]
+    }
+
+    submission_response = requests.post(
+        "https://agents-course-unit4-scoring.hf.space/submit",
+        json=payload
     )
-    return response.json()
 
-with gr.Blocks() as demo:
-    with gr.Row():
-        username = gr.Textbox(label="Hugging Face Username")
-        code_link = gr.Textbox(label="Link to Your Space Code (tree/main)")
-        submit_btn = gr.Button("Submit Answers")
-    output = gr.Textbox(label="Response")
+    print(f"Submission Status: {submission_response.status_code}")
+    print(f"Submission Response: {submission_response.json()}")
+    
+    return state
+
+
+# 4. Build the Graph
+workflow = StateGraph(AgentState)
+
+# Add nodes
+workflow.add_node("fetch_question", fetch_question_node)
+workflow.add_node("download_file", download_file_node)
+workflow.add_node("reason", reason_and_answer_node)
+workflow.add_node("submit", submit_answer_node)
 
-    submit_btn.click(fn=submit_answers, inputs=[username, code_link], outputs=output)
+# Define the graph flow
+workflow.set_entry_point("fetch_question")
+workflow.add_edge("fetch_question", "download_file")
+workflow.add_edge("download_file", "reason")
+workflow.add_edge("reason", "submit")
+workflow.add_edge("submit", END)
 
-demo.launch()
+# Compile the graph
+app = workflow.compile()