app.py

import gradio as gr
import torch
from transformers import (
    TrOCRProcessor, VisionEncoderDecoderModel,
    WhisperProcessor, WhisperForConditionalGeneration,
    MarianMTModel, MarianTokenizer,
    pipeline
)
from PIL import Image
import torchaudio
import numpy as np

print("Loading models...")

# OCR
ocr_processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
ocr_model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-printed")

# Speech to Text
whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-small")
whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")

# Translation
trans_en_es_tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-es")
trans_en_es_model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-es")

trans_es_en_tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-es-en")
trans_es_en_model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-es-en")

# Sentiment
sentiment = pipeline("sentiment-analysis")

# Summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

print("Models loaded successfully")

def ocr_extract(image):
    if image is None:
        return "Error: No image"
    pixel_values = ocr_processor(image, return_tensors="pt").pixel_values
    generated_ids = ocr_model.generate(pixel_values)
    text = ocr_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return text

def speech_to_text(audio):
    if audio is None:
        return "Error: No audio"
    
    audio_array, sr = torchaudio.load(audio)
    if sr != 16000:
        resampler = torchaudio.transforms.Resample(sr, 16000)
        audio_array = resampler(audio_array)
    
    inputs = whisper_processor(audio_array.squeeze().numpy(), sampling_rate=16000, return_tensors="pt")
    generated_ids = whisper_model.generate(inputs.input_features)
    text = whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return text

def translate_en_to_es(text):
    if not text:
        return "Error: No text"
    inputs = trans_en_es_tokenizer(text, return_tensors="pt", padding=True)
    translated = trans_en_es_model.generate(**inputs)
    return trans_en_es_tokenizer.batch_decode(translated, skip_special_tokens=True)[0]

def translate_es_to_en(text):
    if not text:
        return "Error: No text"
    inputs = trans_es_en_tokenizer(text, return_tensors="pt", padding=True)
    translated = trans_es_en_model.generate(**inputs)
    return trans_es_en_tokenizer.batch_decode(translated, skip_special_tokens=True)[0]

def analyze_sentiment(text):
    if not text:
        return "Error: No text"
    result = sentiment(text)[0]
    return f"{result['label']}: {result['score']:.2%}"

def summarize(text):
    if not text or len(text) < 100:
        return "Text too short"
    result = summarizer(text, max_length=130, min_length=30, do_sample=False)
    return result[0]['summary_text']

with gr.Blocks(title="AI Toolkit") as app:
    gr.Markdown("# AI Multi-Model Toolkit")
    
    with gr.Tab("OCR"):
        with gr.Row():
            ocr_img = gr.Image(type="pil", label="Image")
            ocr_out = gr.Textbox(label="Text", lines=10)
        gr.Button("Extract").click(ocr_extract, ocr_img, ocr_out)
    
    with gr.Tab("Speech to Text"):
        with gr.Row():
            audio_in = gr.Audio(type="filepath", label="Audio")
            audio_out = gr.Textbox(label="Transcription", lines=10)
        gr.Button("Transcribe").click(speech_to_text, audio_in, audio_out)
    
    with gr.Tab("Translation"):
        with gr.Row():
            with gr.Column():
                trans_in = gr.Textbox(label="Input", lines=5)
                with gr.Row():
                    btn_en_es = gr.Button("EN to ES")
                    btn_es_en = gr.Button("ES to EN")
            trans_out = gr.Textbox(label="Output", lines=5)
        btn_en_es.click(translate_en_to_es, trans_in, trans_out)
        btn_es_en.click(translate_es_to_en, trans_in, trans_out)
    
    with gr.Tab("Sentiment"):
        with gr.Row():
            sent_in = gr.Textbox(label="Text", lines=5)
            sent_out = gr.Textbox(label="Result", lines=2)
        gr.Button("Analyze").click(analyze_sentiment, sent_in, sent_out)
    
    with gr.Tab("Summarize"):
        with gr.Row():
            summ_in = gr.Textbox(label="Long Text", lines=10)
            summ_out = gr.Textbox(label="Summary", lines=5)
        gr.Button("Summarize").click(summarize, summ_in, summ_out)

app.launch()