Update app.py

app.py

@@ -1,280 +1,52 @@
 import os
-import io
-import requests
 import gradio as gr
-from groq import Groq
-from transformers import MarianMTModel, MarianTokenizer, AutoModelForCausalLM, AutoTokenizer
-from deep_translator import GoogleTranslator
-from PIL import Image, ImageDraw
-import joblib
-import time
-from indic_transliteration import sanscript
-from indic_transliteration.sanscript import transliterate
-import openai
+from transformers import pipeline
+from diffusers import StableDiffusionPipeline
 import torch
-import warnings
-from huggingface_hub import InferenceApi
 
-# Detect if GPU is available
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-# Set up Groq API key
-api_key = os.getenv("GROQ_API_KEY")
-client = Groq(api_key=api_key)
+HF_TOKEN = os.getenv("HF_TOKEN")
 
-# Set your Hugging Face API key
-os.environ['HF_API_KEY']
-api_key = os.getenv('HF_API_KEY')
-if api_key is None:
-    raise ValueError("Hugging Face API key is not set. Please set it in your environment.")
+# 1. Tamil to English translator (public model, no token required)
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
 
-# Set OpenAI API key for text generation
-openai.api_key = os.getenv('OPENAI_API_KEY')
+# 2. English text generator (GPT-2, public model, no token required)
+generator = pipeline("text-generation", model="gpt2")
 
-headers = {"Authorization": f"Bearer {api_key}"}
+# 3. Stable Diffusion image generator (needs token)
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Load GPT-Neo for creative text generation
-text_generation_model_name = "EleutherAI/gpt-neo-1.3B"
-text_generation_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name).to(device)
-text_generation_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name)
+image_pipe = StableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    use_auth_token=HF_TOKEN,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32
+)
+image_pipe = image_pipe.to(device)
 
-# Add padding token to GPT-Neo tokenizer if not present
-if text_generation_tokenizer.pad_token is None:
-    text_generation_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
-
-# Define the API URL for image generation
-API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
-
-# Load the trained sentiment analysis model and preprocessing steps
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    model = joblib.load('model.pkl')
-
-# Function to query Hugging Face API
-def query(payload, max_retries=5):
-    for attempt in range(max_retries):
-        response = requests.post(API_URL, headers=headers, json=payload)
-
-        if response.status_code == 503:
-            print(f"Model is still loading, retrying... Attempt {attempt + 1}/{max_retries}")
-            estimated_time = min(response.json().get("estimated_time", 60), 60)
-            time.sleep(estimated_time)
-            continue
-
-        if response.status_code != 200:
-            print(f"Error: Received status code {response.status_code}")
-            print(f"Response: {response.text}")
-            return None
-
-        return response.content
-
-    print(f"Failed to generate image after {max_retries} attempts.")
-    return None
-
-# Function to generate image
-def generate_image(prompt):
-    image_bytes = query({"inputs": prompt})
-
-    if image_bytes is None:
-        error_img = Image.new('RGB', (300, 300), color=(255, 0, 0))
-        d = ImageDraw.Draw(error_img)
-        d.text((10, 150), "Image Generation Failed", fill=(255, 255, 255))
-        return error_img
-
-    try:
-        image = Image.open(io.BytesIO(image_bytes))
-        return image
-    except Exception as e:
-        print(f"Error: {e}")
-        error_img = Image.new('RGB', (300, 300), color=(255, 0, 0))
-        d = ImageDraw.Draw(error_img)
-        d.text((10, 150), "Invalid Image Data", fill=(255, 255, 255))
-        return error_img
-
-# Tamil Audio to Tamil text
-def transcribe_audio(audio_path):
-    if audio_path is None:
-        return "Please upload an audio file."
-    try:
-        with open(audio_path, "rb") as file:
-            transcription = client.audio.transcriptions.create(
-                file=(os.path.basename(audio_path), file.read()),
-                model="whisper-large-v3",
-                response_format="verbose_json",
-            )
-        return transcription.text
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-# Transliterate Romanized Tamil (in English letters) to Tamil script
-def transliterate_to_tamil(romanized_text):
-    try:
-        # Step 1: Normalize the input for better transliteration results
-        romanized_text = romanized_text.strip().lower()  # Remove extra spaces and convert to lowercase
-
-        # Step 2: Handle common punctuation that might interrupt transliteration
-        romanized_text = romanized_text.replace(".", " ").replace(",", " ").replace("?", " ").replace("!", " ")
-
-        # Step 3: Apply ITRANS transliteration
-        tamil_text = transliterate(romanized_text, sanscript.ITRANS, sanscript.TAMIL)
-
-        return tamil_text
-    except Exception as e:
-        return f"An error occurred during transliteration: {str(e)}"
-
-# Function to translate Tamil text to English using deep-translator
-def translate_tamil_to_english(tamil_text):
-    if not tamil_text:
-        return "Please provide text to translate."
-    try:
-        translator = GoogleTranslator(source='ta', target='en')
-        translated_text = translator.translate(tamil_text)
-
-        # Predict sentiment from translated text
-        sentiment_result = predict_sentiment(translated_text)
-
-        return translated_text, sentiment_result, translated_text
-    except Exception as e:
-        return f"An error occurred during translation: {str(e)}", None, None
-
-# Function to predict sentiment from English text
-def predict_sentiment(english_text):
-    if not english_text:
-        return "No text provided for sentiment analysis."
-    try:
-        sentiment = model.predict([english_text])[0]
-        return f"Sentiment: {sentiment}"
-    except Exception as e:
-        return f"An error occurred during sentiment prediction: {str(e)}"
-
-# Generate creative text based on the translated English text
-def generate_creative_text(english_text):
-    if not english_text:
-        return "Please provide text to generate creative content."
-
-    try:
-        inputs = text_generation_tokenizer(english_text, return_tensors="pt", padding=True, truncation=True).to(device)
-
-        # Set parameters to control the output and avoid repetition
-        generated_tokens = text_generation_model.generate(
-            **inputs,
-            max_length=60,
-            num_return_sequences=1,
-            no_repeat_ngram_size=3,
-            temperature=0.7,
-            top_p=0.9,
-            do_sample=True,
-            early_stopping=True
-        )
-
-        creative_text = text_generation_tokenizer.decode(generated_tokens[0], skip_special_tokens=True).strip()
-        return creative_text
-
-    except Exception as e:
-        return f"An error occurred during text generation: {str(e)}"
+def generate_image_from_tamil(tamil_text):
+    # Translate Tamil → English
+    translated = translator(tamil_text, max_length=100)[0]['translation_text']
+    
+    # Generate English text from translated sentence
+    generated = generator(translated, max_length=50, num_return_sequences=1)[0]['generated_text']
+    
+    # Generate image from generated English text
+    image = image_pipe(generated).images[0]
+    
+    return translated, generated, image
 
 # Create Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        <h1 style='color: #4CAF50;'>🎙️ Tamil Audio Transcription, Translation, Sentiment Prediction, Creative Text Generation, and Image Generation</h1>
-        <p style='color: #000080;'>Upload an audio file to get the Tamil transcription, edit the transcription or type Romanized Tamil to convert it to Tamil script, translate it to English, predict the sentiment of the translated text, generate creative English text, and generate an image.</p>
-        """
-    )
-
-    # Input for audio file
-    with gr.Row():
-        audio_input = gr.Audio(type="filepath", label="Upload Audio File")
-        transcribe_button = gr.Button("Transcribe Audio", elem_id="transcribe_btn")
-
-    # Output field for Tamil transcription with ability to edit or type Romanized Tamil
-    transcription_output = gr.Textbox(label="Transcription (Tamil or Romanized Tamil)", interactive=True ,elem_id="transcription_output")
-
-    # Button for transliterating Romanized Tamil to Tamil script
-    transliterate_button = gr.Button("Convert to Tamil Script", elem_id="transliterate_btn")
-
-    # Input field for Tamil text and translate button
-    with gr.Row():
-        translate_button = gr.Button("Translate to English", elem_id="translate_btn")
-
-    # Output field for English translation
-    translation_output = gr.Textbox(label="Translation (English)", elem_id="translation_output")
-
-    # Output field for sentiment prediction
-    sentiment_output = gr.Textbox(label="Sentiment", elem_id="sentiment_output")
-
-    # Button to generate creative text
-    creative_text_button = gr.Button("Generate Creative Text", elem_id="creative_btn")
-
-    # Output field for creative text
-    creative_text_output = gr.Textbox(label="Creative Text", elem_id="creative_output")
-
-    # Button to generate image
-    generate_button = gr.Button("Generate Image", elem_id="generate_btn")
-
-    # Output field for image file
-    image_output = gr.Image(label="Generated Image")
-
-    # Define variable to hold the translated English text
-    translated_text_var = gr.State()
-
-    # Define button click actions
-    transcribe_button.click(
-        fn=transcribe_audio,
-        inputs=audio_input,
-        outputs=transcription_output,
-    )
-
-    transliterate_button.click(
-        fn=transliterate_to_tamil,
-        inputs=transcription_output,
-        outputs=transcription_output,
-    )
-
-    translate_button.click(
-        fn=translate_tamil_to_english,
-        inputs=transcription_output,
-        outputs=[translation_output, sentiment_output, translated_text_var],
-    )
-
-    creative_text_button.click(
-        fn=generate_creative_text,
-        inputs=translated_text_var,
-        outputs=creative_text_output,
-    )
-
-    generate_button.click(
-        fn=generate_image,
-        inputs=translated_text_var,
-        outputs=image_output,
-    )
-
-# Apply custom CSS
-demo.css = """
-#transcribe_btn, #transliterate_btn, #translate_btn, #creative_btn, #generate_btn {
-    background-color: #05907B; /* Change button color */
-    color: white; /* Change text color */
-}
-
-#translation_output,#transcription_output, #sentiment_output, #creative_output {
-    background-color: #f0f8ff; /* Change background color of text areas */
-}
-
-h1 {
-    color: #4CAF50; /* Main heading color */
-}
-
-p {
-    color: #000080; /* Plain text color */
-}
-
-/* Add thick border to entire app */
-.gradio-container {
-    border: 5px solid #05907B; /* Thick border color */
-    padding: 10px; /* Padding inside the border */
-    border-radius: 10px; /* Optional: add rounded corners */
-}
-"""
-
-# Launch the interface and ensure code stops afterward
-demo.launch(share=True)
+iface = gr.Interface(
+    fn=generate_image_from_tamil,
+    inputs=gr.Textbox(lines=2, label="Enter Tamil Text"),
+    outputs=[
+        gr.Textbox(label="Translated English Text"),
+        gr.Textbox(label="Generated English Prompt"),
+        gr.Image(label="Generated Image")
+    ],
+    title="Tamil Text to English and Image Generator",
+    description="Translate Tamil to English, generate English text, and create image using Stable Diffusion."
+)
+
+# Launch Gradio app with public link
+iface.launch(share=True)