Update app.py

app.py

@@ -4,51 +4,44 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-from PIL import Image
-import base64
-import io
 
-import google.generativeai as genai
-from smolagents import CodeAgent, DuckDuckGoSearchTool, LiteLLMModel
+from smolagents import LiteLLMModel, CodeAgent, DuckDuckGoSearchTool
 
-# System prompt used by the agent
+# System prompt for the agent
 SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question.
-Report your thoughts, and finish your answer with just the answer — no prefixes like \"FINAL ANSWER:\".
+Report your thoughts, and finish your answer with just the answer — no prefixes like "FINAL ANSWER:".
 Your answer should be a number OR as few words as possible OR a comma-separated list of numbers and/or strings.
 If you're asked for a number, don’t use commas or units like $ or %, unless specified.
 If you're asked for a string, don’t use articles or abbreviations (e.g. for cities), and write digits in plain text unless told otherwise."""
 
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# Load GEMINI_API_KEY from environment
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-
-# Agent wrapper
+# Agent wrapper using LiteLLMModel
 class MyAgent:
     def __init__(self):
-        model = LiteLLMModel(model_id="gemini/gemini-1.5-flash", api_key=GEMINI_API_KEY)
-        self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)
-
-    def __call__(self, question: str, code: str | None = None, excel_df: pd.DataFrame | None = None, image: Image.Image | None = None) -> str:
-        if excel_df is not None:
-            preview = excel_df.head().to_csv(index=False)
-            context = f"This is a preview of the attached Excel sales data:\n\n{preview}"
-            prompt = f"{question}\n\n{context}"
-            return self.agent.run(prompt)
-        elif image is not None:
-            buffered = io.BytesIO()
-            image.save(buffered, format="JPEG")
-            img_b64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-            prompt = f"{question}\n\nThis is the attached image (base64 JPEG):\n\n{img_b64}"
-            return self.agent.run(prompt)
-        elif code:
-            formatted = f"{question}\n\nThoughts: Let's analyze the attached code.\nCode:\n```py\n{code}\n```<end_code>"
-            return self.agent.run(formatted)
-        else:
-            return self.agent.run(question)
+        gemini_api_key = os.getenv("GEMINI_API_KEY")
+        if not gemini_api_key:
+            raise ValueError("GEMINI_API_KEY not set in environment variables.")
+        
+        # Instantiate LiteLLMModel with Gemini API key and model id
+        self.model = LiteLLMModel(
+            model_id="gemini/gemini-2.0-flash-lite",
+            api_key=gemini_api_key,
+            system_prompt=SYSTEM_PROMPT
+        )
+        
+        # Create the CodeAgent with optional base tools and DuckDuckGo search
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool()],
+            model=self.model,
+            add_base_tools=True,
+        )
+
+    def __call__(self, question: str) -> str:
+        return self.agent.run(question)
 
 # Main evaluation function
-def run_and_submit_all(profile, uploaded_code: list[gr.File] | None, uploaded_excel: list[gr.File] | None, uploaded_image: list[gr.File] | None):
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
 
     if profile:
@@ -73,30 +66,6 @@ def run_and_submit_all(profile, uploaded_code: list[gr.File] | None, uploaded_ex
     except Exception as e:
         return f"Error fetching questions: {e}", None
 
-    uploaded_code_str = ""
-    if uploaded_code:
-        try:
-            uploaded_file = uploaded_code[0]
-            uploaded_code_str = uploaded_file.read().decode("utf-8")
-        except Exception as e:
-            uploaded_code_str = f"# Failed to load uploaded code: {e}"
-
-    uploaded_excel_df = None
-    if uploaded_excel:
-        try:
-            uploaded_excel_df = pd.read_excel(uploaded_excel[0].name)
-        except Exception as e:
-            print(f"Error reading Excel: {e}")
-            uploaded_excel_df = None
-
-    uploaded_image_obj = None
-    if uploaded_image:
-        try:
-            uploaded_image_obj = Image.open(uploaded_image[0].name)
-        except Exception as e:
-            print(f"Error loading image: {e}")
-            uploaded_image_obj = None
-
     results_log = []
     answers_payload = []
 
@@ -106,14 +75,9 @@ def run_and_submit_all(profile, uploaded_code: list[gr.File] | None, uploaded_ex
         if not task_id or question_text is None:
             continue
         try:
-            answer = agent(
-                question_text,
-                uploaded_code_str if "code" in question_text.lower() else None,
-                uploaded_excel_df if "excel" in question_text.lower() or "spreadsheet" in question_text.lower() else None,
-                uploaded_image_obj if "image" in question_text.lower() or "photo" in question_text.lower() or "jpg" in question_text.lower() else None
-            )
-            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": answer})
+            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:
             results_log.append({
                 "Task ID": task_id,
@@ -152,23 +116,15 @@ with gr.Blocks() as demo:
     **Instructions:**
     1. Clone this space and configure your Gemini API key.
     2. Log in to Hugging Face.
-    3. Optionally upload Python code, Excel file, or image used by the questions.
-    4. Run your agent on evaluation tasks and submit answers.
+    3. Run your agent on evaluation tasks and submit answers.
     """)
 
     gr.LoginButton()
-    code_upload = gr.File(label="Upload Python code file", file_types=[".py"])
-    excel_upload = gr.File(label="Upload Excel file", file_types=[".xls", ".xlsx"])
-    image_upload = gr.File(label="Upload Image file", file_types=[".jpg", ".jpeg"])
     run_button = gr.Button("Run Evaluation & Submit All Answers")
     status_output = gr.Textbox(label="Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Results", wrap=True)
 
-    run_button.click(
-        fn=run_and_submit_all,
-        inputs=[gr.State(), code_upload, excel_upload, image_upload],
-        outputs=[status_output, results_table]
-    )
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
     print("🔧 App starting...")
@@ -176,3 +132,4 @@ if __name__ == "__main__":
 
 
 
+