Last

app.py

@@ -21,7 +21,7 @@ class WebSearchEngine:
         })
         
         # API Keys (set these in environment variables)
-        self.serper_api_key = os.getenv("90f426e61bed9f1ffce51a95b98945531c35279a")  # Get from serper.dev
+        self.serper_api_key = os.getenv("SERPER_API_KEY")  # Get from serper.dev
         self.brave_api_key = os.getenv("BRAVE_API_KEY")    # Get from brave.com/search/api
         self.serpapi_key = os.getenv("SERPAPI_KEY")        # Get from serpapi.com
         

txt.txt

@@ -1,383 +1 @@
-llama-index-core
-llama-index-llms-huggingface
-transformers>=4.30.0
-torch>=2.0.0
-accelerate
-gradio>=4.0.0
-requests
-pandas
-python-dotenv
-duckduckgo-search
-sympy
-sentencepiece
-protobuf
-
-app.py
-# app.py
-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
-import os
-import gradio as gr
-import requests
-import pandas as pd
-import traceback
-
-# Import real tool dependencies
-try:
-    from duckduckgo_search import DDGS
-except ImportError:
-    print("Warning: duckduckgo_search not installed. Web search will be limited.")
-    DDGS = None
-
-try:
-    from sympy import sympify
-    from sympy.core.sympify import SympifyError
-except ImportError:
-    print("Warning: sympy not installed. Math calculator will be limited.")
-    sympify = None
-    SympifyError = Exception
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Advanced Agent Definition ---
-class SmartAgent:
-    def __init__(self):
-        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 with real implementations
-        self.tools = [
-            FunctionTool.from_defaults(
-                fn=self.web_search,
-                name="web_search",
-                description="Searches the web for current information using DuckDuckGo when questions require up-to-date knowledge"
-            ),
-            FunctionTool.from_defaults(
-                fn=self.math_calculator,
-                name="math_calculator",
-                description="Performs mathematical calculations and symbolic math using SymPy when questions involve numbers or equations"
-            )
-        ]
-        
-        # Create ReAct agent with tools
-        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:
-        """Real web search using DuckDuckGo"""
-        print(f"Web search triggered for: {query[:50]}...")
-        
-        if not DDGS:
-            return "Web search unavailable - duckduckgo_search not installed"
-        
-        try:
-            with DDGS() as ddgs:
-                results = list(ddgs.text(query, max_results=3))
-                if results:
-                    formatted_results = []
-                    for i, r in enumerate(results, 1):
-                        title = r.get('title', 'No title')
-                        body = r.get('body', 'No description')[:200]
-                        url = r.get('href', '')
-                        formatted_results.append(f"{i}. {title}\n{body}...\nSource: {url}")
-                    return "\n\n".join(formatted_results)
-                else:
-                    return "No search results found for the query."
-        except Exception as e:
-            print(f"Web search error: {e}")
-            return f"Error during web search: {str(e)}"
-
-    def math_calculator(self, expression: str) -> str:
-        """Safe math evaluation using SymPy"""
-        print(f"Math calculation triggered for: {expression}")
-        
-        if not sympify:
-            # Fallback to basic eval with safety checks
-            try:
-                # Only allow basic math operations
-                allowed_chars = set('0123456789+-*/().^ ')
-                if not all(c in allowed_chars for c in expression.replace(' ', '')):
-                    return "Error: Only basic math operations are allowed"
-                result = eval(expression.replace('^', '**'))
-                return str(result)
-            except Exception as e:
-                return f"Error: Could not evaluate the mathematical expression - {str(e)}"
-        
-        try:
-            # Use SymPy for safe evaluation
-            result = sympify(expression).evalf()
-            return str(result)
-        except SympifyError as e:
-            return f"Error: Could not parse the mathematical expression - {str(e)}"
-        except Exception as e:
-            return f"Error: Calculation failed - {str(e)}"
-
-    def __call__(self, question: str) -> str:
-        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)}")
-            print(f"Full traceback: {traceback.format_exc()}")
-            return f"Error processing question: {str(e)}"
-
-
-# --- Submission Logic ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the agent on them, submits all answers,
-    and displays the results.
-    """
-    space_id = os.getenv("SPACE_ID")
-
-    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"
-
-    # Instantiate Agent
-    try:
-        agent = SmartAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        print(f"Full traceback: {traceback.format_exc()}")
-        return f"Error initializing agent: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(f"Agent code URL: {agent_code}")
-
-    # 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}")
-        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
-
-    # Run Agent on all questions
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    
-    for i, item in enumerate(questions_data, 1):
-        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
-            
-        print(f"Processing question {i}/{len(questions_data)}: {task_id}")
-        
-        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[:100] + "..." if len(question_text) > 100 else question_text,
-                "Submitted Answer": submitted_answer[:200] + "..." if len(submitted_answer) > 200 else submitted_answer
-            })
-            print(f"✅ Completed question {i}: {task_id}")
-        except Exception as e:
-            print(f"❌ Error running agent on task {task_id}: {e}")
-            error_answer = f"AGENT ERROR: {str(e)}"
-            answers_payload.append({"task_id": task_id, "submitted_answer": error_answer})
-            results_log.append({
-                "Task ID": task_id, 
-                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
-                "Submitted Answer": error_answer
-            })
-
-    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)
-
-    # Prepare submission
-    submission_data = {
-        "username": username.strip(), 
-        "agent_code": agent_code, 
-        "answers": answers_payload
-    }
-    
-    status_update = f"Agent finished processing. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # Submit answers
-    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\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
-
-
-# --- Gradio UI ---
-with gr.Blocks(title="Local LLM Agent Evaluation") as demo:
-    gr.Markdown("# 🤖 Local LLM Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-        1. 🔐 Log in to your Hugging Face account using the button below
-        2. 🚀 Click 'Run Evaluation & Submit All Answers'
-        3. ⏳ Wait for the local LLM (Zephyr-7B) to process all questions
-        4. 📊 View your results and submission status
-        
-        **Features:**
-        - 🔍 Real web search using DuckDuckGo
-        - 🧮 Advanced math calculations with SymPy
-        - 🧠 Powered by HuggingFace Zephyr-7B model
-        """
-    )
-
-    with gr.Row():
-        gr.LoginButton()
-    
-    with gr.Row():
-        run_button = gr.Button(
-            "🚀 Run Evaluation & Submit All Answers", 
-            variant="primary", 
-            size="lg"
-        )
-    
-    status_output = gr.Textbox(
-        label="📋 Run Status / Submission Result", 
-        lines=8, 
-        interactive=False,
-        placeholder="Click the button above to start the evaluation..."
-    )
-    
-    results_table = gr.DataFrame(
-        label="📊 Questions and Agent Answers", 
-        wrap=True,
-        interactive=False
-    )
-
-    # Wire up the button
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-
-if __name__ == "__main__":
-    print("\n" + "="*60)
-    print("🚀 Application Startup at", pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S"))
-    print("="*60)
-    
-    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}")
-    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?).")
-
-    print("-" * 60)
-    print("🎯 Launching Gradio Interface for Local LLM Agent Evaluation...")
-    
-    # Launch without share=True for Hugging Face Spaces
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
-
-readme
----
-title: Template Final Assignment
-emoji: 🕵🏻‍♂️
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.25.2
-app_file: app.py
-pinned: false
-hf_oauth: true
-# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
-hf_oauth_expiration_minutes: 480
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+"90f426e61bed9f1ffce51a95b98945531c35279a"