Update app.py

app.py

@@ -1,126 +1,155 @@
-# app.py
 import os
-import gradio as gr
 import requests
-import pandas as pd
-from agent import agent
-import asyncio  
-import nest_asyncio   # ← NEW
-nest_asyncio.apply()  # ← NEW
-# Constants
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# Async helper to run the agent
-async def run_agent(agent, question_text):
-    return await agent.run(question_text)
-
-# Gradio Agent Interface
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the LlamaIndexAgent on them, 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
-
-    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"
-
-    # 1. Instantiate LlamaIndexAgent
-    print("Using imported agent instance.")
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 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
-
-    # 3. Run your LlamaIndex 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
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from langchain_community.document_loaders import WikipediaLoader
+from llama_index.core.tools.types import ToolMetadata
+from llama_index.core.schema import Document
+from llama_index.core.tools import FunctionTool
+from langchain_community.tools.tavily_search import TavilySearchResults
+from llama_index.core.agent.workflow import AgentWorkflow
+
+hf_token = os.getenv("HF_TOKEN")
+
+# List of models to try in order
+model_list = [
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "microsoft/phi-3-mini-128k-instruct",
+    "google/gemma-2b-it",
+    "gpt2"
+]
+
+current_model_index = 0
+llm = HuggingFaceInferenceAPI(
+    model_name=model_list[current_model_index],
+    token=hf_token,
+)
+
+# Numerical operation functions
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+def add(a: int, b: int) -> int:
+    """Add two numbers."""
+    return a + b
+
+def subtract(a: int, b: int) -> int:
+    """Subtract two numbers."""
+    return a - b
+
+def divide(a: int, b: int) -> float:
+    """Divide two numbers, raises error on zero divisor."""
+    if b == 0:
+        raise ValueError("Cannot divide by zero.")
+    return a / b
+
+def modulus(a: int, b: int) -> int:
+    """Get the modulus of two numbers."""
+    return a % b
+
+# Web search tool function
+def web_search(query: str) -> list:
+    """Search Tavily for a query and return up to 3 results."""
+    results = TavilySearchResults(max_results=3).invoke(query=query)
+    docs = []
+    for r in results:
+        meta = {"source": r.metadata.get("source", ""), "page": r.metadata.get("page", "")}
+        docs.append(Document(text=r.page_content, metadata=meta))
+    return docs
+
+# Wikipedia search tool function
+def wiki_search(query: str) -> list:
+    """Search Wikipedia for a query and return up to 2 results."""
+    results = WikipediaLoader(query=query, load_max_docs=2).load()
+    docs = []
+    for r in results:
+        meta = {"source": r.metadata.get("source", ""), "page": r.metadata.get("page", "")}
+        docs.append(Document(text=r.page_content, metadata=meta))
+    return docs
+
+# Wrap functions into FunctionTool instances
+web_search_tool = FunctionTool(
+    web_search,
+    metadata=ToolMetadata(name="web_search", description="Tavily 3-hit search")
+)
+wiki_search_tool = FunctionTool(
+    wiki_search,
+    metadata=ToolMetadata(name="wiki_search", description="Wikipedia 2-hit search")
+)
+
+multiply_tool = FunctionTool(multiply, metadata=ToolMetadata(name="multiply", description="Multiply two numbers."))
+add_tool      = FunctionTool(add,      metadata=ToolMetadata(name="add",      description="Add two numbers."))
+subtract_tool = FunctionTool(subtract, metadata=ToolMetadata(name="subtract", description="Subtract two numbers."))
+divide_tool   = FunctionTool(divide,   metadata=ToolMetadata(name="divide",   description="Divide two numbers."))
+modulus_tool  = FunctionTool(modulus,  metadata=ToolMetadata(name="modulus",  description="Modulus operation on two numbers."))
+
+# Aggregate all tools
+tools = [
+    web_search_tool,
+    wiki_search_tool,
+    multiply_tool,
+    add_tool,
+    subtract_tool,
+    divide_tool,
+    modulus_tool,
+]
+
+# Initialize agent
+agent = AgentWorkflow.from_tools_or_functions(tools, llm=llm)
+
+# Function to try the next model in the list
+def try_next_model():
+    """Switch to the next model in the list and reinitialize the agent.
+    Returns True if successful, False if we've tried all models."""
+    global current_model_index, llm, agent
+    
+    current_model_index += 1
+    if current_model_index >= len(model_list):
+        return False
+    
+    # Reinitialize LLM with new model
+    llm = HuggingFaceInferenceAPI(
+        model_name=model_list[current_model_index],
+        token=hf_token,
+    )
+    
+    # Reinitialize agent with new LLM
+    agent = AgentWorkflow.from_tools_or_functions(tools, llm=llm)
+    return True
+
+# Run with fallback logic
+def run_with_fallback(query: str):
+    global current_model_index, llm, agent
+    
+    # Reset to first model if we're not already on it
+    if current_model_index != 0:
+        current_model_index = 0
+        llm = HuggingFaceInferenceAPI(
+            model_name=model_list[current_model_index],
+            token=hf_token,
+        )
+        agent = AgentWorkflow.from_tools_or_functions(tools, llm=llm)
+    
+    # Try each model in sequence
+    for i in range(len(model_list)):
         try:
-            loop = asyncio.get_event_loop()  # Get the current event loop
-            submitted_answer = loop.run_until_complete(run_agent(agent, question_text))
-            if not isinstance(submitted_answer, (str, dict, list, int, float, bool, type(None))):
-                submitted_answer = str(submitted_answer)
-            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})
+            result = agent.run(query)
+            print(f"Successfully ran query with model: {model_list[current_model_index]}")
+            return result
         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.RequestException as e:
-        print(f"Submission failed: {e}")
-        return f"Submission failed: {e}", pd.DataFrame(results_log)
-
-
-# Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("# LlamaIndex Agent Evaluation Runner")
+            print(f"Error with model {model_list[current_model_index]}: {e}")
+            if i < len(model_list) - 1:  # If not the last model
+                try_next_model()
+            else:
+                break
     
-    gr.LoginButton()
+    return "Sorry, encountered issues with all models."
 
-    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=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+# Make agent.run() work with asyncio by adding async support
+async def run(query: str):
+    """Async wrapper for the agent.run method to be compatible with app.py"""
+    return run_with_fallback(query)
 
-if __name__ == "__main__":
-    print("Launching Gradio Interface for LlamaIndex Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+# Add the async run method to the agent object
+agent.run = run_with_fallback  # Replace with synchronous version for direct calls