Update app.py

app.py

@@ -1,83 +1,87 @@
 import gradio as gr
-import pandas as pd
 import json
-from smolagents import CodeAgent, InferenceClientModel
+import pandas as pd
+from smolagents import CodeAgent
+from smolagents.models import InferenceClientModel
 from smolagents.tools import (
     DuckDuckGoSearchTool,
     WikipediaSearchTool,
     PythonInterpreterTool,
-    UserInputTool
+    UserInputTool,
 )
 
-# -------------------------- MODEL SETUP -------------------------- #
+# Define the model
 model = InferenceClientModel(
-    model_id="Qwen/Qwen2-7B-Instruct",  # You can change this to another Hugging Face Inference-compatible model
-    system_message="You are a helpful AI assistant for answering academic questions in a concise and accurate way.",
+    model_id="Qwen/Qwen1.5-1.8B-Chat",  # Ensure this is a model that supports system_message
+    system_message="You are a helpful academic assistant tasked with solving student questions accurately.",
     max_tokens=512,
 )
 
-# -------------------------- TOOLS SETUP -------------------------- #
+# Define tools
 tools = [
-    DuckDuckGoSearchTool(max_results=5, rate_limit=2.0),
-    WikipediaSearchTool(language="en", user_agent="my-eval-agent"),
+    DuckDuckGoSearchTool(),
+    WikipediaSearchTool(),
     PythonInterpreterTool(),
-    UserInputTool()
+    UserInputTool(),
 ]
 
-# -------------------------- AGENT SETUP -------------------------- #
-agent = CodeAgent(
-    model=model,
-    tools=tools
-)
+# Initialize agent
+agent = CodeAgent(model=model, tools=tools)
 
-# -------------------------- EVALUATION FUNCTION -------------------------- #
+# Run agent on uploaded question set
 def run_agent(file):
+    results_log = []
+    answers_payload = []
+
     try:
-        questions_data = json.load(file)
+        with open(file.name, "r", encoding="utf-8") as f:
+            questions_data = json.load(f)
     except Exception as e:
-        return f"Invalid JSON: {e}", pd.DataFrame()
+        return f"Error reading JSON file: {e}", pd.DataFrame()
 
-    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:
+        question = item.get("question")
+
+        if not task_id or question is None:
+            results_log.append({
+                "Task ID": task_id if task_id else "MISSING",
+                "Question": question if question else "MISSING",
+                "Submitted Answer": "Invalid entry",
+            })
             continue
 
         try:
-            answer = agent.run(question_text)
+            answer = agent(question)
             answers_payload.append({"task_id": task_id, "submitted_answer": answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": answer})
+            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})
         except Exception as e:
             error_msg = f"AGENT ERROR: {e}"
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": error_msg})
+            print(error_msg)
+            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": error_msg})
 
     if not answers_payload:
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    results_df = pd.DataFrame(results_log)
-    return "Agent run completed!", results_df
+    return "Success! Answers submitted.", pd.DataFrame(results_log)
 
-# -------------------------- GRADIO UI -------------------------- #
+# Create Gradio UI
 demo = gr.Interface(
     fn=run_agent,
-    inputs=gr.File(label="Upload questions.json"),
+    inputs=gr.File(label="Upload your questions.json"),
     outputs=[
         gr.Textbox(label="Status"),
-        gr.Dataframe(label="Results", wrap=True),
+        gr.Dataframe(label="Submitted Answers")
     ],
-    title="SmolAgent Question Evaluator",
-    description="Upload a JSON file with `task_id` and `question` fields to evaluate the agent's performance.",
+    title="Academic Assistant Agent",
+    description="Upload a .json file with questions and task_ids to run the agent.",
 )
 
 if __name__ == "__main__":
     demo.launch()
 
-
-
 # import os
 # import pandas as pd
 # import requests