Create app.py

app.py

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+import streamlit as st
+import base64
+import os
+import requests
+from transformers import MarianMTModel, MarianTokenizer, AutoModelForCausalLM, AutoTokenizer
+from PIL import Image, ImageDraw
+import io
+import torch
+
+# Detect if GPU is available
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load the MarianMT model and tokenizer for translation (Tamil to English)
+model_name = "Helsinki-NLP/opus-mt-mul-en"
+translation_model = MarianMTModel.from_pretrained(model_name).to(device)
+translation_tokenizer = MarianTokenizer.from_pretrained(model_name, clean_up_tokenization_spaces=True)
+
+# Set padding token for the translation tokenizer
+translation_tokenizer.pad_token = translation_tokenizer.eos_token
+
+# 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, clean_up_tokenization_spaces=True)
+
+# Set padding token for the text generation tokenizer
+text_generation_tokenizer.pad_token = text_generation_tokenizer.eos_token
+
+# Retrieve Hugging Face API key from environment variable
+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.")
+
+headers = {"Authorization": f"Bearer {api_key}"}
+
+# Define the API URL for image generation
+API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
+
+# Unsplash API for background images
+UNSPLASH_URL = "https://api.unsplash.com/photos/random"
+UNSPLASH_API_KEY = "YOUR_UNSPLASH_API_KEY"  # Replace with your Unsplash API key
+
+
+# Function to fetch a random wallpaper from Unsplash
+def fetch_random_wallpaper():
+    response = requests.get(UNSPLASH_URL, headers={"Authorization": f"Client-ID {UNSPLASH_API_KEY}"})
+    if response.status_code == 200:
+        data = response.json()
+        image_url = data['urls']['full']
+        return image_url
+    else:
+        st.error("Failed to fetch wallpaper from Unsplash.")
+        return None
+
+# Function to set background image
+def set_background_image(image_url):
+    if image_url is not None:
+        # Fetch the image from the URL
+        image_bytes = requests.get(image_url).content
+        b64 = base64.b64encode(image_bytes).decode()
+        # Set the CSS style to set the background image
+        st.markdown(
+            f"""
+            <style>
+            .stApp {{
+                background-image: url(data:image/png;base64,{b64});
+                background-size: cover;
+                background-position: center;
+                height: 100vh;
+                width: 100vw;
+            }}
+            </style>
+            """,
+            unsafe_allow_html=True
+        )
+
+# Query Hugging Face API to generate image with error handling
+def query(payload):
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload)
+        response.raise_for_status()  # Raise an error for bad status codes
+        return response.content
+    except requests.exceptions.RequestException as e:
+        st.error(f"API call failed: {e}")
+        return None
+
+# Translate Tamil text to English
+def translate_text(tamil_text):
+    inputs = translation_tokenizer(tamil_text, return_tensors="pt", padding=True, truncation=True).to(device)
+    translated_tokens = translation_model.generate(**inputs)
+    translation = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return translation
+
+# Generate an image based on the translated text with error handling
+def generate_image(prompt):
+    image_bytes = query({"inputs": prompt})
+
+    if image_bytes is None:
+        # Return a blank image with an error message
+        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)).convert("RGB")
+        return image
+    except Exception as e:
+        st.error(f"Error loading image: {e}")
+        # Return an error image in case of failure
+        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
+
+# Generate creative text based on the translated English text
+def generate_creative_text(translated_text):
+    inputs = text_generation_tokenizer(translated_text, return_tensors="pt", padding=True, truncation=True).to(device)
+    generated_tokens = text_generation_model.generate(**inputs, max_length=100)
+    creative_text = text_generation_tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
+    return creative_text
+
+# Function to handle the full workflow
+def translate_generate_image_and_text(tamil_text):
+    # Step 1: Translate Tamil to English
+    translated_text = translate_text(tamil_text)
+
+    # Step 2: Generate an image from the translated text
+    image = generate_image(translated_text)
+
+    # Step 3: Generate creative text from the translated text
+    creative_text = generate_creative_text(translated_text)
+
+    return translated_text, creative_text, image
+
+# Streamlit app title and subtitle
+st.markdown("<h1 style='text-align: center;'>A Multimodal Application</h1>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: center;'>Language Translation and Image Synthesis</h3>", unsafe_allow_html=True)
+
+# Fetch and set random wallpaper from Unsplash
+image_url = fetch_random_wallpaper()
+set_background_image(image_url)
+
+# User input for Tamil text
+tamil_input = st.text_area("Enter Tamil Text", height=150)
+
+# Button to generate translation, creative text, and image
+if st.button("Generate"):
+    if tamil_input:
+        with st.spinner("Generating..."):
+            translated_output, creative_text_output, generated_image_output = translate_generate_image_and_text(tamil_input)
+        
+        st.subheader("Translated Text")
+        st.write(translated_output)
+        
+        st.subheader("Creative Generated Text")
+        st.write(creative_text_output)
+        
+        st.subheader("Generated Image")
+        st.image(generated_image_output, caption="Generated Image", use_column_width=True)
+    else:
+        st.warning("Please enter some Tamil text.")