Update app.py

app.py

@@ -1,6 +1,5 @@
-from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, load_tool, tool
 import xml.etree.ElementTree as ET
-import matplotlib.pyplot as plt
 from wordcloud import WordCloud
 from collections import Counter
 import re
@@ -9,10 +8,10 @@ import requests
 import pytz
 import yaml
 from tools.final_answer import FinalAnswerTool
+import gradio as gr
+import os
 
-from Gradio_UI import GradioUI
-
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
+# Below is an example of a tool that does nothing. Amaze us with your creativity!
 @tool
 def search_arxiv(query: str):
     """Searches arXiv for academic papers and returns structured results.
@@ -53,6 +52,8 @@ def generate_visuals(query):
     words = [word.lower() for title in titles for word in re.findall(r'\b\w+\b', title) if len(word) > 3]
     word_counts = Counter(words).most_common(10)
 
+    # Save Bar Chart Image
+    bar_chart_path = "/tmp/bar_chart.png"
     plt.figure(figsize=(8, 5))
     plt.bar(*zip(*word_counts), color='skyblue')
     plt.xticks(rotation=45)
@@ -60,18 +61,13 @@ def generate_visuals(query):
     plt.xlabel("Keywords")
     plt.ylabel("Frequency")
     plt.tight_layout()
-    bar_chart_path = "bar_chart.png"
     plt.savefig(bar_chart_path)
     plt.close()
 
     # Generate Word Cloud for Summary Text
     wordcloud = WordCloud(width=500, height=300, background_color="white").generate(summaries)
-    plt.figure(figsize=(8, 5))
-    plt.imshow(wordcloud, interpolation="bilinear")
-    plt.axis("off")
-    wordcloud_path = "wordcloud.png"
-    plt.savefig(wordcloud_path)
-    plt.close()
+    wordcloud_path = "/tmp/wordcloud.png"
+    wordcloud.to_file(wordcloud_path)
 
     # Display Search Results as Clickable Links
     markdown_text = "\n\n".join(
@@ -80,7 +76,6 @@ def generate_visuals(query):
 
     return markdown_text, bar_chart_path, wordcloud_path
 
-
 @tool
 def summarize_text(text: str) -> str:
     """Summarizes long academic papers or articles.
@@ -112,26 +107,21 @@ def get_current_time_in_timezone(timezone: str) -> str:
 
 final_answer = FinalAnswerTool()
 
-# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
-
 model = HfApiModel(
-max_tokens=2096,
-temperature=0.5,
-model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-custom_role_conversions=None,
+    max_tokens=2096,
+    temperature=0.5,
+    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
 )
 
-
 # Import tool from Hub
 image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
 
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
-    
+
 agent = CodeAgent(
     model=model,
-    tools=[final_answer, search_arxiv, summarize_text], ## add your tools here (don't remove final answer)
+    tools=[final_answer, search_arxiv, summarize_text],  # add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,
@@ -141,6 +131,7 @@ agent = CodeAgent(
     prompt_templates=prompt_templates
 )
 
+# Gradio Interface for arXiv research search and visualization
 iface = gr.Interface(
     fn=generate_visuals,
     inputs="text",
@@ -150,6 +141,8 @@ iface = gr.Interface(
     examples=[["Machine Learning"], ["Quantum Computing"], ["Climate Change"]]
 )
 
+# Launch Gradio Interface
 iface.launch()
 
-GradioUI(agent).launch()
+# The Gradio UI component (not needed if you already have the Gradio interface launched above)
+# GradioUI(agent).launch()