import gradio as gr
from transformers import pipeline
import spacy
import subprocess
import sys

# Download en_core_web_sm model if not available
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    # If the model is not present, download it
    subprocess.run([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
    nlp = spacy.load("en_core_web_sm")

# Load the Hugging Face model for text classification
classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")

# Function for text classification
def classify_text(text_content):
    results = classifier(text_content)
    classifications = [(result['label'], result['score']) for result in results]
    return classifications

# Function for syntax analysis
def analyze_syntax(text_content):
    doc = nlp(text_content)
    syntax_data = [
        {
            "Token": token.text,
            "Part of Speech": token.pos_,
            "Lemma": token.lemma_,
            "Dependency": token.dep_,
        }
        for token in doc
    ]
    return syntax_data

# Gradio Interface
def classify_and_analyze(text_content):
    classifications = classify_text(text_content)
    syntax_analysis = analyze_syntax(text_content)
    return classifications, syntax_analysis

# Define Gradio interface with two outputs
iface = gr.Interface(
    fn=classify_and_analyze,
    inputs="text",
    outputs=[
        gr.outputs.Dataframe(headers=["Label", "Confidence"], label="Classification Results"),
        gr.outputs.Dataframe(headers=["Token", "Part of Speech", "Lemma", "Dependency"], label="Syntax Analysis"),
    ],
    title="Text Classification and Syntax Analysis Tool",
    description="Analyze text classification and syntax with Hugging Face Transformers and SpaCy.",
)

iface.launch()