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| import os | |
| import gradio as gr | |
| import google.generativeai as genai | |
| import spacy | |
| import yake | |
| import subprocess | |
| # Ensure spaCy model is downloaded dynamically | |
| MODEL_NAME = "en_core_web_sm" | |
| try: | |
| nlp = spacy.load(MODEL_NAME) | |
| except OSError: | |
| subprocess.run(["python", "-m", "spacy", "download", MODEL_NAME]) | |
| nlp = spacy.load(MODEL_NAME) | |
| # Configure Google Gemini AI | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Use environment variable for security | |
| def analyze_text(text): | |
| """Perform AI-driven text analysis.""" | |
| if not text: | |
| return "Please enter some text." | |
| # Word Count | |
| word_count = len(text.split()) | |
| # Summarization using Google Gemini AI | |
| try: | |
| prompt = f"Summarize this text:\n{text}" | |
| model = genai.GenerativeModel(model_name="gemini-2.0-flash") | |
| response = model.generate_content([prompt]) # Ensure the prompt is passed as a list | |
| summary = response.text.strip() if response and hasattr(response, "text") else "Error in summarization." | |
| except Exception as e: | |
| summary = f"Summarization failed: {str(e)}" | |
| # Basic Sentiment Analysis | |
| sentiment = "Positive" if "good" in text.lower() else "Negative" | |
| # Keyword Extraction | |
| kw_extractor = yake.KeywordExtractor() | |
| keywords = [kw[0] for kw in kw_extractor.extract_keywords(text)[:5]] | |
| # Named Entity Recognition (NER) | |
| doc = nlp(text) | |
| entities = {ent.text: ent.label_ for ent in doc.ents} | |
| # AI-Generated Report | |
| report = f""" | |
| \n**Summary:** {summary} | |
| \n**Sentiment:** {sentiment} | |
| \n**Keywords:** {', '.join(keywords)} | |
| \n**Entities:** {entities if entities else 'None'} | |
| \n**Word Count:** {word_count} | |
| """ | |
| return report | |
| # Gradio Interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# AI-Powered Text & File Analyzer 🚀") | |
| input_text = gr.Textbox(label="Enter Text") | |
| file_input = gr.File(label="Upload .txt File", file_types=[".txt"]) | |
| analyze_button = gr.Button("Analyze") | |
| output = gr.Markdown() | |
| def process_input(text, file): | |
| """Process text from input or file.""" | |
| if file: | |
| with open(file.name, "r", encoding="utf-8") as f: | |
| text = f.read() | |
| return analyze_text(text) | |
| analyze_button.click(process_input, inputs=[input_text, file_input], outputs=output) | |
| demo.launch() |