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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("AIzaSyDnx_qUjGTFG1pv1otPUhNt_bGGv14aMDI")) # Use environment variable for security
def analyze_text(text):
"""Perform AI-driven text analysis."""
if not text:
return "Please enter some text."
# Summarization using Google Gemini AI
try:
prompt = f"Summarize this text:\n{text}"
model = genai.GenerativeModel("gemini-pro")
response = model.generate_content(prompt)
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 (Improve with ML if needed)
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"""
**Summary:** {summary}
**Sentiment:** {sentiment}
**Keywords:** {', '.join(keywords)}
**Entities:** {entities if entities else 'None'}
"""
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()
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