Update app.py

app.py

@@ -1,242 +1,177 @@
 import os
 import gradio as gr
-import requests
-import pandas as pd
 from dotenv import load_dotenv
-from langchain_openai import ChatOpenAI
-from langchain_nvidia_ai_endpoints import ChatNVIDIA
-from langchain_groq import ChatGroq
+from langgraph.graph import START, StateGraph, MessagesState
+from langgraph.prebuilt import tools_condition, ToolNode
 from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.tools import tool
+from supabase import create_client, Client
 
 # Load environment variables
 load_dotenv()
 
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-class BasicAgent:
-    def __init__(self, provider="nvidia"):
-        self.provider = provider.lower()
-        if self.provider == "nvidia":
-            self.llm = ChatNVIDIA(
-                model="meta/llama-3.3-70b-instruct",
-                nvidia_api_key=os.getenv("NVIDIA_API_KEY")
-            )
-        elif self.provider == "groq":
-            self.llm = ChatGroq(
-                model="llama3-70b-8192",
-                api_key=os.getenv("GROQ_API_KEY")
-            )
-        elif self.provider == "google":
-            self.llm = ChatGoogleGenerativeAI(
-                model="gemini-2.0-flash",
-                temperature=0.1,
-                max_tokens=1024,
-                api_key=os.getenv("GOOGLE_API_KEY"),
-                streaming=False
-            )
-        elif self.provider == "openai":
-            self.llm = ChatOpenAI(
-                model="gpt-3.5-turbo",
-                api_key=os.getenv("OPENAI_API_KEY")
+# Tool definitions remain unchanged
+@tool
+def multiply(a: int, b: int) -> int:
+    return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+    return a + b
+
+@tool
+def subtract(a: int, b: int) -> int:
+    return a - b
+
+@tool
+def divide(a: int, b: int) -> int:
+    if b == 0:
+        raise ValueError("Cannot divide by zero.")
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    return a % b
+
+@tool
+def wiki_search(query: str) -> str:
+    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [f'<Document source="{doc.metadata["source"]}"/>\n{doc.page_content}\n</Document>' 
+         for doc in search_docs])
+    return {"wiki_results": formatted_search_docs}
+
+@tool
+def web_search(query: str) -> str:
+    search_docs = TavilySearchResults(max_results=3).invoke(query)
+    formatted_search_docs = "\n\n---\n\n".join(
+        [f'<Document source="{doc.metadata["source"]}"/>\n{doc.page_content}\n</Document>' 
+         for doc in search_docs])
+    return {"web_results": formatted_search_docs}
+
+@tool
+def arvix_search(query: str) -> str:
+    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [f'<Document source="{doc.metadata["source"]}"/>\n{doc.page_content[:1000]}\n</Document>' 
+         for doc in search_docs])
+    return {"arvix_results": formatted_search_docs}
+
+# System prompt definition
+SYSTEM_PROMPT = """You are a helpful assistant. For every question, reply with only the answer—no explanation, 
+no units, and no extra words. If the answer is a number, just return the number. 
+If it is a word or phrase, return only that. If it is a list, return a comma-separated list with no extra words. 
+Do not include any prefix, suffix, or explanation."""
+sys_msg = SystemMessage(content=SYSTEM_PROMPT)
+
+# Initialize vector store
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+supabase: Client = create_client(
+    os.environ["SUPABASE_URL"], 
+    os.environ["SUPABASE_SERVICE_KEY"]
+)
+vector_store = SupabaseVectorStore(
+    client=supabase,
+    embedding=embeddings,
+    table_name="documents",
+    query_name="match_documents_langchain",
+)
+
+tools = [multiply, add, subtract, divide, modulus, 
+         wiki_search, web_search, arvix_search]
+
+# Build graph function with multi-provider support
+def build_graph(provider: str = "groq"):
+    # Provider selection
+    if provider == "google":
+        llm = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash", 
+            temperature=0,
+            api_key=os.getenv("GOOGLE_API_KEY")
+        )
+    elif provider == "groq":
+        llm = ChatGroq(
+            model="llama3-70b-8192", 
+            temperature=0,
+            api_key=os.getenv("GROQ_API_KEY")
+        )
+    elif provider == "huggingface":
+        llm = ChatHuggingFace(
+            llm=HuggingFaceEndpoint(
+                endpoint_url="https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2",
+                temperature=0,
+                api_key=os.getenv("HF_API_KEY")
             )
-        else:
-            raise ValueError("Unsupported provider. Choose from: nvidia, groq, google, openai.")
-
-        self.instructions = (
-            "You are a helpful assistant. For every question, reply with only the answer—no explanation, "
-            "no units, and no extra words. If the answer is a number, just return the number. "
-            "If it is a word or phrase, return only that. If it is a list, return a comma-separated list with no extra words. "
-            "Do not include any prefix, suffix, or explanation."
         )
-        print(f"BasicAgent initialized with provider: {self.provider}")
-
-    def __call__(self, question: str) -> str:
-        prompt = f"{self.instructions}\n\n{question}"
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        response = self.llm.invoke(prompt)
-        answer = response.content.strip() if hasattr(response, "content") else str(response)
-        # Remove "FINAL ANSWER:" or similar prefixes if present
-        for prefix in ["FINAL ANSWER:", "Final answer:", "final answer:"]:
-            if answer.lower().startswith(prefix.lower()):
-                answer = answer[len(prefix):].strip()
-        print(f"Agent returning answer: {answer}")
-        return answer
-
-def run_and_submit_all(profile: gr.OAuthProfile | None, provider="nvidia"):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    space_id = os.getenv("SPACE_ID")  # For codebase link
-
-    if profile:
-        username = f"{profile.username}"
-        print(f"User logged in: {username}")
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+    
+    llm_with_tools = llm.bind_tools(tools)
+    
+    # Graph nodes
+    def retriever(state: MessagesState):
+        similar_question = vector_store.similarity_search(state["messages"][-1].content, k=1)
+        if similar_question:
+            example_msg = HumanMessage(content=f"Similar reference: {similar_question[0].page_content[:200]}...")
+            return {"messages": state["messages"] + [example_msg]}
+        return {"messages": state["messages"]}
+    
+    def assistant(state: MessagesState):
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    
+    # Build graph
+    builder = StateGraph(MessagesState)
+    builder.add_node("retriever", retriever)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    
+    builder.add_edge(START, "retriever")
+    builder.add_edge("retriever", "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
+    
+    return builder.compile()
 
-    # 1. Instantiate Agent
+# Gradio interface
+def run_agent(question, provider):
     try:
-        agent = BasicAgent(provider=provider)
+        graph = build_graph(provider)
+        messages = [HumanMessage(content=question)]
+        result = graph.invoke({"messages": messages})
+        final_answer = result["messages"][-1].content
+        return final_answer
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+        return f"Error: {str(e)}"
 
-    # 2. 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
-        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
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        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)
-            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:
-            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}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        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}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-# --- Build Gradio Interface using Blocks ---
+# Create Gradio interface
 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.  Select your preferred provider and 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 separate action or even to answer the questions in async.
-        """
-    )
-
-    gr.LoginButton()
-
-    provider_dropdown = gr.Dropdown(
-        choices=["nvidia", "groq", "google", "openai"],
-        value="nvidia",
-        label="Choose LLM Provider"
+    gr.Markdown("## LangGraph Multi-Provider Agent")
+    
+    provider = gr.Dropdown(
+        choices=["groq", "google", "huggingface"],
+        value="groq",
+        label="LLM Provider"
     )
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=lambda profile, provider: run_and_submit_all(profile, provider),
-        inputs=[gr.OAuthProfile(), provider_dropdown],
-        outputs=[status_output, results_table]
+    
+    question = gr.Textbox(label="Your Question")
+    submit_btn = gr.Button("Run Agent")
+    output = gr.Textbox(label="Agent Response", interactive=False)
+    
+    submit_btn.click(
+        fn=run_agent,
+        inputs=[question, provider],
+        outputs=output
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    space_host_startup = os.getenv("SPACE_HOST")
-    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(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...")
-    demo.launch(debug=True, share=False)
-
+    demo.launch()