Update app.py

app.py

@@ -1,4 +1,4 @@
-from transformers import MBartForConditionalGeneration, MBart50Tokenizer
+from transformers import MBartForConditionalGeneration, MBart50Tokenizer, AutoModelForCausalLM, AutoTokenizer, pipeline
 import gradio as gr
 import requests
 import io
@@ -21,6 +21,14 @@ else:
 # Define the text-to-image model URL (using a stable diffusion model)
 API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
 
+# Load the text generation model for generating detailed paragraphs
+text_generation_model_name = "EleutherAI/gpt-neo-2.7B"
+text_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name)
+text_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name)
+
+# Create a pipeline for text generation
+text_generator = pipeline("text-generation", model=text_model, tokenizer=text_tokenizer)
+
 # Function to generate an image using Hugging Face's text-to-image model
 def generate_image_from_text(translated_text):
     try:
@@ -41,8 +49,20 @@ def generate_image_from_text(translated_text):
         print(f"Error during image generation: {e}")
         return None, f"Error during image generation: {e}"
 
-# Define the function to translate Tamil text and generate an image
-def translate_and_generate_image(tamil_text):
+# Function to generate a detailed paragraph from the translated text
+def generate_paragraph_from_text(translated_text):
+    try:
+        print(f"Generating paragraph from translated text: {translated_text}")
+        # Generate detailed text from translated text using the text generation model
+        paragraph = text_generator(translated_text, max_length=250, num_return_sequences=1, temperature=0.7, top_p=0.9)[0]['generated_text']
+        print(f"Paragraph generation completed: {paragraph}")
+        return paragraph
+    except Exception as e:
+        print(f"Error during paragraph generation: {e}")
+        return f"Error during paragraph generation: {e}"
+
+# Define the function to translate Tamil text, generate a paragraph, and create an image
+def translate_generate_paragraph_and_image(tamil_text):
     # Step 1: Translate Tamil text to English using mbart-large-50
     try:
         print("Translating Tamil text to English...")
@@ -52,24 +72,30 @@ def translate_and_generate_image(tamil_text):
         translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
         print(f"Translation completed: {translated_text}")
     except Exception as e:
-        return f"Error during translation: {e}", None
+        return f"Error during translation: {e}", "", None, None
+
+    # Step 2: Generate a detailed paragraph based on the translated English text
+    paragraph = generate_paragraph_from_text(translated_text)
+    if "Error" in paragraph:
+        return translated_text, paragraph, None, None
 
-    # Step 2: Directly generate an image using the translated English text
+    # Step 3: Generate an image using the translated English text
     image, error_message = generate_image_from_text(translated_text)
     if error_message:
-        return translated_text, error_message
+        return translated_text, paragraph, None, error_message
 
-    return translated_text, image
+    return translated_text, paragraph, image, None
 
 # Gradio interface setup
 iface = gr.Interface(
-    fn=translate_and_generate_image,
+    fn=translate_generate_paragraph_and_image,
     inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text here..."),
     outputs=[gr.Textbox(label="Translated English Text"), 
+             gr.Textbox(label="Generated Descriptive Paragraph"),
              gr.Image(label="Generated Image")],
-    title="Tamil to English Translation and Image Creation",
-    description="Translate Tamil text to English using Facebook's mbart-large-50 model and create an image using the translated text.",
+    title="Tamil to English Translation, Paragraph Generation, and Image Creation",
+    description="Translate Tamil text to English using Facebook's mbart-large-50 model, generate a detailed paragraph, and create an image using the translated text.",
 )
 
 # Launch Gradio app without `share=True`
-iface.launch()
+iface.launch()