Upload gen_ai_project_f.py

gen_ai_project_f.py

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+# -*- coding: utf-8 -*-
+"""gen ai project f.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1iF7hdOjWNeFUtGvUYdaFsBErJGnY1h5J
+"""
+
+# Install necessary packages
+!pip install transformers torch diffusers streamlit gradio huggingface_hub
+!pip install pyngrok  # For exposing the app to the public
+!pip install sacremoses
+!pip install sentencepiece
+
+from huggingface_hub import login
+
+login(token="hf_gen")
+
+!pip install requests
+!pip install Pillow
+
+# Import necessary libraries
+from transformers import MarianMTModel, MarianTokenizer, pipeline
+
+# Load the translation model and tokenizer
+model_name = "Helsinki-NLP/opus-mt-mul-en"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+
+# Create a translation pipeline
+translator = pipeline("translation", model=model, tokenizer=tokenizer)
+
+# Function for translation
+def translate_text(tamil_text):
+    try:
+        # Perform translation
+        translation = translator(tamil_text, max_length=40)
+        translated_text = translation[0]['translation_text']
+        return translated_text
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Test translation with example Tamil text
+tamil_text = "மழையுடன் ஒரு பூ"  # "A flower with rain"
+translated_text = translate_text(tamil_text)
+print(f"Translated Text: {translated_text}")
+
+import requests
+import io
+from PIL import Image
+import matplotlib.pyplot as plt
+
+# API credentials and endpoint
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+headers = {"Authorization": "Bearer hf_gen"}
+
+# Function to send payload and generate image
+def generate_image(prompt):
+    try:
+        # Send request to API
+        response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+
+        # Check if the response is successful
+        if response.status_code == 200:
+            print("API call successful, generating image...")
+            image_bytes = response.content
+
+            # Try opening the image
+            try:
+                image = Image.open(io.BytesIO(image_bytes))
+                return image
+            except Exception as e:
+                print(f"Error opening image: {e}")
+        else:
+            # Handle non-200 responses
+            print(f"Failed to get image: Status code {response.status_code}")
+            print("Response content:", response.text)  # Print response for debugging
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+# Display image
+def show_image(image):
+    if image:
+        plt.imshow(image)
+        plt.axis('off')  # Hide axes
+        plt.show()
+    else:
+        print("No image to display")
+
+# Test the function with a prompt
+prompt = "A flower with rain"
+image = generate_image(prompt)
+
+# Display the generated image
+show_image(image)
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load GPT-Neo model for creative text generation
+gpt_neo_tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
+gpt_neo_model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
+
+# Function to generate creative text based on translated text
+def generate_creative_text(translated_text):
+    input_ids = gpt_neo_tokenizer(translated_text, return_tensors='pt').input_ids
+    generated_text_ids = gpt_neo_model.generate(input_ids, max_length=100)
+    creative_text = gpt_neo_tokenizer.decode(generated_text_ids[0], skip_special_tokens=True)
+    return creative_text
+
+import gradio as gr
+
+# Function to handle the full workflow
+def translate_generate_image_and_text(tamil_text):
+    # Step 1: Translate Tamil text to English
+    translated_text = translate_text(tamil_text)
+
+    # Step 2: Generate an image based on the translated text
+    image = generate_image(translated_text)
+
+    # Step 3: Generate creative text based on the translated text
+    creative_text = generate_creative_text(translated_text)
+
+    return translated_text, creative_text, image
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=translate_generate_image_and_text,
+    inputs="text",
+    outputs=["text", "text", "image"],
+    title="Tamil to English Translation, Image Generation & Creative Text",
+    description="Enter Tamil text to translate to English, generate an image, and create creative text based on the translation."
+)
+
+# Launch Gradio app
+interface.launch()