Update app.py

app.py

@@ -3,8 +3,7 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel # <--- CHANGE THIS LINE
-# (Keep Constants and BasicAgent class as is)
+from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel # Make sure InferenceClientModel is imported
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -13,18 +12,57 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
-        # self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel()) # <--- OLD LINE
-        self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=InferenceClientModel()) # <--- CHANGE THIS LINE
-        SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and
-        finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
-        YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated
-        list of numbers and/or strings.
-        If you are asked for a number, don't use comma to write your number neither use units such as $ or
-        percent sign unless specified otherwise.
-        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the
-        digits in plain text unless specified otherwise.
-        If you are asked for a comma separated list, apply the above rules depending of whether the element
-        to be put in the list is a number or a string.
+
+        # --- CHOOSE THE BEST MODEL HERE ---
+        # Using Meta-Llama-3-8B-Instruct for a good balance of performance and cost.
+        # Ensure you have accepted its terms on Hugging Face and have sufficient
+        # Inference Provider credits or a PRO subscription with pay-as-you-go enabled.
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool()],
+            model=InferenceClientModel(model_id="meta-llama/Meta-Llama-3-8B-Instruct"),
+            max_steps=10, # Increase max_steps for more complex reasoning chains if needed
+            # use_structured_outputs_internally=True # Optional: Can sometimes improve performance for capable models, but might also cause issues if the model doesn't strictly adhere. Test this if needed.
+        )
+
+        # --- REFINED SYSTEM PROMPT ---
+        SYSTEM_PROMPT = """You are a highly capable and precise AI assistant. Your primary goal is to answer questions accurately and concisely, following strict formatting rules.
+
+        **Here's how you should approach each question:**
+        1.  **Understand the Request:** Read the question carefully to identify what information is needed and what type of answer is expected (number, string, or list).
+        2.  **Gather Information:**
+            * First, try to answer based on your internal knowledge if the information is common or involves simple logic/arithmetic.
+            * If the question requires factual knowledge, current information, or is complex, **you MUST use the `DuckDuckGoSearchTool`**. Formulate precise search queries to get the most relevant results.
+            * Analyze the search results meticulously to extract the exact information required.
+        3.  **Reason Step-by-Step:** Before providing the final answer, explicitly state your thought process. This helps in complex reasoning. Use a "Thought:" section.
+        4.  **Formulate FINAL ANSWER:** Construct your final answer strictly according to these rules:
+            * **Template:** Always end your entire response with `FINAL ANSWER: [YOUR FINAL ANSWER]`.
+            * **Numbers:** If the answer is a number, write it in digits ONLY, without commas, unit symbols (e.g., "$", "%", "km", "kg"), or text (e.g., "one" instead of "1"). For example, "12345" not "12,345" or "12345 dollars".
+            * **Strings (words):** If the answer is a string, use as few words as possible. Do NOT use articles (a, an, the). Do NOT use abbreviations. If a number is part of a string, write it in plain English words (e.g., "twenty" instead of "20"), unless it's a specific numerical value (like a year or an ID).
+            * **Comma-separated lists:** If the answer is a list, apply the above rules to each element in the list and separate elements with a comma and a single space (e.g., "apple, banana, orange" or "one, two, three").
+
+        **Examples of desired FINAL ANSWER formats:**
+
+        * **Question:** What is the capital of Japan?
+            * **Thought:** I know the capital of Japan is a specific city.
+            * **FINAL ANSWER: Tokyo**
+
+        * **Question:** How many days are in a common year?
+            * **Thought:** A common year has a fixed number of days.
+            * **FINAL ANSWER: three hundred sixty five**
+
+        * **Question:** Who developed Python and in what year?
+            * **Thought:** This requires factual recall of an inventor and a year. I will use DuckDuckGoSearchTool if uncertain.
+            * **FINAL ANSWER: Guido van Rossum, nineteen ninety one**
+
+        * **Question:** List three primary colors.
+            * **Thought:** I need to list common primary colors.
+            * **FINAL ANSWER: red, yellow, blue**
+
+        * **Question:** Calculate 15 multiplied by 7.
+            * **Thought:** This is a simple arithmetic calculation.
+            * **FINAL ANSWER: one hundred five**
+
+        Your detailed thought process (after "Thought:") should lead directly to the accurate `FINAL ANSWER` based on the specified format.
         """
         self.agent.prompt_templates["system_prompt"] = self.agent.prompt_templates["system_prompt"] + SYSTEM_PROMPT
             
@@ -60,8 +98,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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/ghanemfaouri/Final_Assignment_Template/tree/main"
-    print(agent_code)
+    # IMPORTANT: Update this to YOUR SPACE_ID if you've cloned it!
+    agent_code = f"https://huggingface.co/spaces/{os.getenv('SPACE_ID', 'ghanemfaouri/Final_Assignment_Template')}/tree/main"
+    print(f"Agent code will link to: {agent_code}") # Added a print for clarity
 
     # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
@@ -165,8 +204,8 @@ with gr.Blocks() as demo:
     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(
         fn=run_and_submit_all,
         outputs=[status_output, results_table]
@@ -174,15 +213,14 @@ 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")