Initial commit with LlamaIndex-based agent

app.py

@@ -1,27 +1,79 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+from llama_index.llms.huggingface import HuggingFaceLLM
+from llama_index.core.agent import ReActAgent
+from llama_index.core.tools import FunctionTool
+from transformers import AutoTokenizer
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+# --- Advanced Agent Definition ---
+class SmartAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Initializing Local LLM Agent...")
+        
+        # Initialize Zephyr-7B model
+        self.llm = HuggingFaceLLM(
+            model_name="HuggingFaceH4/zephyr-7b-beta",
+            tokenizer_name="HuggingFaceH4/zephyr-7b-beta",
+            context_window=2048,
+            max_new_tokens=256,
+            generate_kwargs={"temperature": 0.7, "do_sample": True},
+            device_map="auto"
+        )
+        
+        # Define tools
+        self.tools = [
+            FunctionTool.from_defaults(
+                fn=self.web_search,
+                name="web_search",
+                description="Searches the web for current information when questions require up-to-date knowledge"
+            ),
+            FunctionTool.from_defaults(
+                fn=self.math_calculator,
+                name="math_calculator",
+                description="Performs mathematical calculations when questions involve numbers or equations"
+            )
+        ]
+        
+        # Create agent
+        self.agent = ReActAgent.from_tools(
+            tools=self.tools,
+            llm=self.llm,
+            verbose=True
+        )
+        print("Local LLM Agent initialized successfully.")
+
+    def web_search(self, query: str) -> str:
+        """Simulated web search tool (replace with actual API)"""
+        print(f"Web search triggered for: {query[:50]}...")
+        return f"Web results for: {query} (implement actual search API here)"
+
+    def math_calculator(self, expression: str) -> str:
+        """Simple math calculator"""
+        print(f"Math calculation triggered for: {expression}")
+        try:
+            result = eval(expression)  # Note: In production, use safer eval alternatives
+            return str(result)
+        except:
+            return "Error: Could not evaluate the mathematical expression"
+
     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
+        print(f"Processing question (first 50 chars): {question[:50]}...")
+        try:
+            response = self.agent.query(question)
+            return str(response)
+        except Exception as e:
+            print(f"Agent error: {str(e)}")
+            return f"Error processing question: {str(e)}"
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+# --- Original Submission Logic (Keep unchanged) ---
+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 agent on them, submits all answers,
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
@@ -38,13 +90,13 @@ 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)
+    # 1. Instantiate Agent
     try:
-        agent = BasicAgent()
+        agent = SmartAgent()  # Using our new SmartAgent instead of BasicAgent
     except Exception as e:
         print(f"Error instantiating 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)
 
@@ -139,31 +191,21 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
-# --- Build Gradio Interface using Blocks ---
+# --- Build Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# Local LLM 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.
+        1. Log in to your Hugging Face account
+        2. Click 'Run Evaluation & Submit All Answers'
+        3. Wait for the local LLM to process all questions
         """
     )
 
     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(
@@ -173,9 +215,8 @@ 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}")
@@ -183,14 +224,13 @@ if __name__ == "__main__":
     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("ℹ️  SPACE_ID environment variable not found (running locally?).")
 
     print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    print("Launching Gradio Interface for Local LLM Agent Evaluation...")
     demo.launch(debug=True, share=False)

requirements.txt

@@ -1,2 +1,8 @@
 gradio
-requests
+requests
+llama-index
+transformers
+torch
+accelerate
+duckduckgo-search  # For web search tools
+python-dotenv  # For API keys (if needed)