Update app.py

app.py

@@ -4,19 +4,65 @@ import requests
 import pytz
 import yaml
 from tools.final_answer import FinalAnswerTool
+from tools.pdf_extractor import extract_text_from_pdf
 
 from Gradio_UI import GradioUI
 
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
 @tool
-def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
-    #Keep this format for the description / args / args description but feel free to modify the tool
-    """A tool that does nothing yet 
+def summarize_and_analyze_text(text: str, max_sentences: int = 5) -> str:
+    """Analyzes and summarizes text content, extracting key information and main ideas.
+
+    This tool intelligently condenses lengthy text into concise summaries while preserving
+    the most important information. Perfect for processing search results, PDFs, and documents.
+
     Args:
-        arg1: the first argument
-        arg2: the second argument
+        text: The text content to summarize and analyze
+        max_sentences: Maximum number of sentences in the summary (default: 5)
+
+    Returns:
+        A formatted summary containing key points and main ideas from the text
     """
-    return "What magic will you build ?"
+    try:
+        # Remove extra whitespace and normalize text
+        text = " ".join(text.split())
+
+        if len(text) < 100:
+            return f"Text is too short to summarize. Original text:\n{text}"
+
+        # Split into sentences (simple approach)
+        sentences = []
+        import re
+        for sent in re.split(r'(?<=[.!?])\s+', text):
+            sent = sent.strip()
+            if sent:
+                sentences.append(sent)
+
+        # Score sentences based on word frequency
+        words = text.lower().split()
+        word_freq = {}
+        for word in words:
+            if len(word) > 3:  # Filter short words
+                word_freq[word] = word_freq.get(word, 0) + 1
+
+        # Select top sentences
+        sentence_scores = []
+        for i, sent in enumerate(sentences):
+            score = sum(word_freq.get(word.lower(), 0) for word in sent.split())
+            sentence_scores.append((i, score, sent))
+
+        # Sort by original order but select based on scores
+        top_indices = sorted([idx for idx, _, _ in sorted(sentence_scores, key=lambda x: -x[1])[:max_sentences]])
+        summary_sentences = [sent for idx, _, sent in sentence_scores if idx in top_indices]
+
+        summary = " ".join(summary_sentences)
+
+        # Extract key entities (words that appear frequently)
+        sorted_words = sorted(word_freq.items(), key=lambda x: -x[1])
+        key_terms = ", ".join([word for word, _ in sorted_words[:5]])
+
+        return f"""📋 SUMMARY:\n{summary}\n\n🔑 KEY TERMS: {key_terms}\n\n📊 ANALYSIS:\n- Text length: {len(text)} characters\n- Total sentences: {len(sentences)}\n- Summary length: {len(summary_sentences)} sentences"""
+    except Exception as e:
+        return f"Error analyzing text: {str(e)}"
 
 @tool
 def get_current_time_in_timezone(timezone: str) -> str:
@@ -52,10 +98,10 @@ image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_co
 
 with open("prompts.yaml", 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
-    
+
 agent = CodeAgent(
     model=model,
-    tools=[final_answer], ## add your tools here (don't remove final answer)
+    tools=[image_generation_tool,get_current_time_in_timezone,extract_text_from_pdf,summarize_and_analyze_text,final_answer], ## add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,
@@ -66,4 +112,4 @@ agent = CodeAgent(
 )
 
 
-GradioUI(agent).launch()
+GradioUI(agent, "/tmp").launch()