app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
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

@tool
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:
        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
    """
    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:
    """A tool that fetches the current local time in a specified timezone.
    Args:
        timezone: A string representing a valid timezone (e.g., 'America/New_York').
    """
    try:
        # Create timezone object
        tz = pytz.timezone(timezone)
        # Get current time in that timezone
        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
        return f"The current local time in {timezone} is: {local_time}"
    except Exception as e:
        return f"Error fetching time for timezone '{timezone}': {str(e)}"


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,
)


# 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=[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,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)


GradioUI(agent, "/tmp").launch()