Update app.py

app.py

@@ -1,41 +1,96 @@
 import os
 import gradio as gr
 from PIL import Image
-from smolagents import (
-    CodeAgent,
-    LiteLLMModel,          # <-- NEW: for Gemini
-    DuckDuckGoSearchTool,
-    Tool
-)
 import requests
-import inspect
 import pandas as pd
+import io
 
-# (Keep Constants as is)
-# --- Constants ---
+# Import LangChain and LangGraph components
+from langgraph.prebuilt import create_react_agent
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain.tools import tool
+
+# Constants (unchanged)
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+# ------------------------------------------------------------------
+# 1. Define your LangGraph ReAct Agent with Gemini 2.0 Flash
+# ------------------------------------------------------------------
+def get_agent():
+    """Initialize the LangGraph ReAct agent with Gemini 2.0 Flash and tools."""
+    # Gemini API key from Hugging Face secrets
+    gemini_api_key = os.getenv("GEMINI_API_KEY")
+    if not gemini_api_key:
+        raise ValueError("GEMINI_API_KEY environment variable not set. Please add it to Hugging Face Space secrets.")
+
+    # Initialize the Gemini model
+    model = ChatGoogleGenerativeAI(
+        model="gemini-2.0-flash",
+        api_key=gemini_api_key,
+        temperature=0.7,
+        timeout=60,
+        max_retries=2
+    )
+
+    # --------------------------------------------------------------
+    # 2. Define your custom tools using LangChain's @tool decorator
+    # --------------------------------------------------------------
+
+    # Tool: DuckDuckGo Search (free web search)
+    ddg_search = DuckDuckGoSearchRun()
+
+    # Tool: Image generation using FLUX.1 Schnell on Hugging Face
+    @tool
+    def image_generator(prompt: str) -> dict:
+        """
+        Generates a high-quality visual image based on a descriptive text prompt.
+        Uses FLUX.1 Schnell from Hugging Face.
+        Returns the image as a base64 string or URL.
+        """
+        HF_TOKEN = os.getenv("HF_TOKEN")  # Optional but helps with rate limits
+        API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
+        headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+
+        response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+        if response.status_code == 200:
+            return {
+                "success": True,
+                "image": response.content,
+                "message": "Image generated successfully."
+            }
+        else:
+            return {
+                "success": False,
+                "message": f"Image generation failed: {response.status_code} - {response.text}"
+            }
+
+    # Combine tools into a list
+    tools = [ddg_search, image_generator]
+
+    # --------------------------------------------------------------
+    # 3. Create the ReAct agent with LangGraph
+    # --------------------------------------------------------------
+    agent = create_react_agent(
+        model=model,
+        tools=tools,
+        prompt=(
+            "You are a helpful AI assistant with access to web search and image generation. "
+            "Always think step by step before using tools. For image requests, use the image_generator tool. "
+            "For web information, use the duckduckgo_search tool."
+        )
+    )
+    
+    return agent
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    Fetches all questions, runs the LangGraph ReAct agent, submits all answers,
     and displays the results.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
+    space_id = os.getenv("SPACE_ID")
     if profile:
-        username= f"{profile.username}"
+        username = profile.username
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -45,75 +100,34 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # ------------------------------------------------------------------
+    # Initialize LangGraph ReAct Agent
+    # ------------------------------------------------------------------
     try:
-        
-        # 2. Instantiate Internet Search & Browsing Tools
-        ddg_search = DuckDuckGoSearchTool()
-        
-        # 3. Create an Image Execution Tool from the Hugging Face Spaces Engine
-        image_generation_tool = Tool.from_space(
-            space_id="black-forest-labs/FLUX.1-schnell", 
-            name="image_generator", 
-            description="Generates a high-quality visual image based on a descriptive text prompt. Returns a PIL Image object."
-        )
-        
-        # 4. Consolidate your toolbox array
-        all_tools = [
-            ddg_search, 
-            #visit_webpage, 
-            image_generation_tool
-        ]
-        
-        # 5. Initialize Google Gemini 2.0 Flash model using LiteLLMModel
-        #    The API key must be stored as a secret named GEMINI_API_KEY in the HF Space.
-        gemini_api_key = os.getenv("GEMINI_API_KEY")
-        if not gemini_api_key:
-            raise ValueError("GEMINI_API_KEY environment variable not set. Please add it to Hugging Face Space secrets.")
-        model = LiteLLMModel(
-            model_id="gemini/gemini-2.0-flash",
-            api_key=gemini_api_key
-        )
-        
-        # 6. Initialize the CodeAgent with the Gemini model
-        agent = CodeAgent(
-            tools=all_tools,
-            model=model,
-            add_base_tools=True,
-            additional_authorized_imports=["time", "math", "json", "PIL"]
-        )
+        agent = get_agent()
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
+        print(f"Error initializing LangGraph agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
-    # 2. Fetch Questions
+    # Fetch questions
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
+            return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+        return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # Run agent on each question
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+    print(f"Running LangGraph ReAct agent on {len(questions_data)} questions...")
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
@@ -121,23 +135,38 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            # LangGraph agent invocation returns a dict with 'output' key
+            result = agent.invoke({
+                "messages": [("user", question_text)]
+            })
+            submitted_answer = result["output"]
             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})
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": submitted_answer
+            })
         except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            print(f"Error running agent on task {task_id}: {e}")
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": f"AGENT ERROR: {e}"
+            })
 
     if not answers_payload:
-        print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    # Prepare submission
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
-    # 5. Submit
+    # Submit answers
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -153,58 +182,34 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print("Submission successful.")
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
+        status_message = f"Submission Failed: {e}"
         print(status_message)
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
-# --- Build Gradio Interface using Blocks ---
+# ------------------------------------------------------------------
+# Gradio Interface (unchanged)
+# ------------------------------------------------------------------
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
-
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
 
         ---
         **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        Once clicking on the "submit" button, it can take quite some time (this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution.
         """
     )
 
     gr.LoginButton()
-
     run_button = gr.Button("Run Evaluation & Submit All Answers")
-
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -214,24 +219,19 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
+    space_id_startup = os.getenv("SPACE_ID")
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
         print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    if space_id_startup:
         print(f"✅ SPACE_ID found: {space_id_startup}")
         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
     else:
         print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
     print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    print("Launching Gradio Interface for LangGraph ReAct Agent Evaluation...")
     demo.launch(debug=True, share=False)