Add text analysis tool and update project dependencies

- Introduced new text_analysis.py tool with comprehensive text analysis capabilities
- Updated requirements.txt with new dependencies for text analysis and project maintenance
- Integrated analyze_text tool into the CodeAgent in app.py

app.py

@@ -4,6 +4,7 @@ import requests
 import pytz
 import yaml
 from tools.final_answer import FinalAnswerTool
+from tools.text_analysis import analyze_text
 
 from Gradio_UI import GradioUI
 
@@ -55,7 +56,7 @@ with open("prompts.yaml", "r") as stream:
 
 agent = CodeAgent(
     model=model,
-    tools=[final_answer],  ## add your tools here (don't remove final answer)
+    tools=[final_answer, analyze_text],  # Added analyze_text tool
     max_steps=6,
     verbosity_level=1,
     grammar=None,

requirements.txt

@@ -1,5 +1,24 @@
-markdownify
-smolagents
-requests
-duckduckgo_search
-pandas
+# Core dependencies
+smolagents>=0.1.0
+gradio>=4.0.0
+pyyaml>=6.0.1
+pytz>=2024.1
+requests>=2.31.0
+
+# For text analysis and NLP
+textblob>=0.17.1
+nltk>=3.8.1
+
+# For web scraping and URL processing
+beautifulsoup4>=4.12.3
+validators>=0.22.0
+
+# For data visualization
+matplotlib>=3.8.2
+numpy>=1.26.4
+
+# Development dependencies
+pytest>=8.0.0
+black>=24.1.1
+isort>=5.13.2
+mypy>=1.8.0

tools/text_analysis.py

@@ -0,0 +1,86 @@
+from typing import Dict, List, Union
+import logging
+from textblob import TextBlob
+import nltk
+from nltk.tokenize import sent_tokenize, word_tokenize
+from nltk.corpus import stopwords
+from smolagents import tool
+
+# Set up logging
+logger = logging.getLogger(__name__)
+
+# Download required NLTK data
+try:
+    nltk.download('punkt', quiet=True)
+    nltk.download('stopwords', quiet=True)
+    nltk.download('averaged_perceptron_tagger', quiet=True)
+except Exception as e:
+    logger.error(f"Failed to download NLTK data: {e}")
+
+@tool
+def analyze_text(text: str) -> Dict[str, Union[str, float, List[str]]]:
+    """Performs comprehensive text analysis including sentiment, readability, and key phrases.
+    
+    Args:
+        text: The input text to analyze
+        
+    Returns:
+        Dict containing analysis results including:
+        - sentiment: Dict with polarity and subjectivity scores
+        - key_phrases: List of important noun phrases
+        - readability: Basic readability metrics
+        - summary: Brief statistical summary
+    """
+    try:
+        # Create TextBlob object
+        blob = TextBlob(text)
+        
+        # Sentiment analysis
+        sentiment = {
+            "polarity": round(blob.sentiment.polarity, 2),
+            "subjectivity": round(blob.sentiment.subjectivity, 2),
+            "sentiment_label": "positive" if blob.sentiment.polarity > 0 else "negative" if blob.sentiment.polarity < 0 else "neutral"
+        }
+        
+        # Extract key phrases (noun phrases)
+        key_phrases = list(set([str(phrase) for phrase in blob.noun_phrases]))[:5]
+        
+        # Basic text statistics
+        sentences = sent_tokenize(text)
+        words = word_tokenize(text)
+        words_no_stop = [word.lower() for word in words 
+                        if word.lower() not in stopwords.words('english')
+                        and word.isalnum()]
+        
+        # Calculate readability (basic metric based on sentence and word length)
+        avg_sentence_length = len(words) / len(sentences)
+        avg_word_length = sum(len(word) for word in words_no_stop) / len(words_no_stop)
+        readability_score = round((avg_sentence_length * 0.39) + (avg_word_length * 11.8) - 15.59, 1)
+        
+        # Prepare response
+        analysis_result = {
+            "sentiment": sentiment,
+            "key_phrases": key_phrases,
+            "readability": {
+                "score": readability_score,
+                "avg_sentence_length": round(avg_sentence_length, 1),
+                "avg_word_length": round(avg_word_length, 1)
+            },
+            "summary": {
+                "sentence_count": len(sentences),
+                "word_count": len(words),
+                "unique_words": len(set(words_no_stop))
+            }
+        }
+        
+        return analysis_result
+    
+    except Exception as e:
+        logger.error(f"Error in text analysis: {e}")
+        return {
+            "error": f"Analysis failed: {str(e)}",
+            "sentiment": {"polarity": 0, "subjectivity": 0, "sentiment_label": "error"},
+            "key_phrases": [],
+            "readability": {"score": 0, "avg_sentence_length": 0, "avg_word_length": 0},
+            "summary": {"sentence_count": 0, "word_count": 0, "unique_words": 0}
+        }