Update Dockerfile

Dockerfile

@@ -1,42 +1,109 @@
-# Base image
-FROM python:3.10
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    git \
-    git-lfs \
-    ffmpeg \
-    libsm6 \
-    libxext6 \
-    cmake \
-    rsync \
-    libgl1-mesa-glx && \
-    rm -rf /var/lib/apt/lists/* && \
-    git lfs install
-
-# Set the working directory
-WORKDIR /home/user/app
-
-# Install Python dependencies
-RUN pip install --no-cache-dir pip==22.3.1 && \
-    pip install --no-cache-dir \
-    datasets \
-    "huggingface-hub>=0.19" \
-    "hf-transfer>=0.1.4" \
-    "protobuf<4" \
-    "click<8.1" \
-    "pydantic~=1.0"
-
-# Clone CorrectLy repository and install it manually
-RUN git clone https://github.com/rounakdatta/CorrectLy.git /home/user/CorrectLy && \
-    pip install -r /home/user/CorrectLy/requirements.txt
-
-# Copy the project requirements file and install other dependencies
-COPY requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -r /tmp/requirements.txt
-
-# Set the user to run the application
-USER 1000
-
-# Command to run the application
-CMD ["python", "app.py"]
+import os
+import gradio as gr
+from transformers import pipeline
+import spacy
+import subprocess
+import nltk
+from nltk.corpus import wordnet
+
+# Initialize the English text classification pipeline for AI detection
+pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
+
+# Function to predict the label and score for English text (AI Detection)
+def predict_en(text):
+    res = pipeline_en(text)[0]
+    return res['label'], res['score']
+
+# Ensure necessary NLTK data is downloaded for Humanifier
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+
+# Ensure the SpaCy model is installed for Humanifier
+try:
+    nlp = spacy.load("en_core_web_sm")
+except OSError:
+    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
+    nlp = spacy.load("en_core_web_sm")
+
+# Grammar, Tense, and Singular/Plural Correction Functions
+
+# Correct article errors (e.g., "a apple" -> "an apple")
+def check_article_error(text):
+    tokens = nltk.pos_tag(nltk.word_tokenize(text))
+    corrected_tokens = []
+    
+    for i, token in enumerate(tokens):
+        word, pos = token
+        if word.lower() == 'a' and i < len(tokens) - 1 and tokens[i + 1][1] == 'NN':
+            corrected_tokens.append('an' if tokens[i + 1][0][0] in 'aeiou' else 'a')
+        else:
+            corrected_tokens.append(word)
+    
+    return ' '.join(corrected_tokens)
+
+# Correct tense errors (e.g., "She has go out" -> "She has gone out")
+def check_tense_error(text):
+    tokens = nltk.pos_tag(nltk.word_tokenize(text))
+    corrected_tokens = []
+    
+    for word, pos in tokens:
+        if word == "go" and pos == "VB":
+            corrected_tokens.append("gone")
+        elif word == "know" and pos == "VB":
+            corrected_tokens.append("known")
+        else:
+            corrected_tokens.append(word)
+    
+    return ' '.join(corrected_tokens)
+
+# Correct singular/plural errors (e.g., "There are many chocolate" -> "There are many chocolates")
+def check_pluralization_error(text):
+    tokens = nltk.pos_tag(nltk.word_tokenize(text))
+    corrected_tokens = []
+    
+    for word, pos in tokens:
+        if word == "chocolate" and pos == "NN":
+            corrected_tokens.append("chocolates")
+        elif word == "kids" and pos == "NNS":
+            corrected_tokens.append("kid")
+        else:
+            corrected_tokens.append(word)
+    
+    return ' '.join(corrected_tokens)
+
+# Combined function to correct grammar, tense, and singular/plural errors
+def correct_grammar_tense_plural(text):
+    text = check_article_error(text)
+    text = check_tense_error(text)
+    text = check_pluralization_error(text)
+    return text
+
+# Gradio app setup with three tabs
+with gr.Blocks() as demo:
+    with gr.Tab("AI Detection"):
+        t1 = gr.Textbox(lines=5, label='Text')
+        button1 = gr.Button("🤖 Predict!")
+        label1 = gr.Textbox(lines=1, label='Predicted Label 🎃')
+        score1 = gr.Textbox(lines=1, label='Prob')
+
+        # Connect the prediction function to the button
+        button1.click(predict_en, inputs=[t1], outputs=[label1, score1], api_name='predict_en')
+    
+    with gr.Tab("Humanifier"):
+        text_input = gr.Textbox(lines=5, label="Input Text")
+        paraphrase_button = gr.Button("Paraphrase & Correct")
+        output_text = gr.Textbox(label="Paraphrased Text")
+
+        # Connect the paraphrasing function to the button
+        paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)
+    
+    with gr.Tab("Grammar Correction"):
+        grammar_input = gr.Textbox(lines=5, label="Input Text")
+        grammar_button = gr.Button("Correct Grammar")
+        grammar_output = gr.Textbox(label="Corrected Text")
+
+        # Connect the custom grammar, tense, and plural correction function to the button
+        grammar_button.click(correct_grammar_tense_plural, inputs=grammar_input, outputs=grammar_output)
+
+# Launch the app with all functionalities
+demo.launch()