Update app.py

app.py

@@ -1,182 +1,47 @@
-# Import required libraries
-import gradio as gr
-import requests
-from getpass import getpass
-import openai
-from PIL import Image
-import io
+import streamlit as st
+from transformers import pipeline
+from diffusers import StableDiffusionPipeline
+import torch
 
-# Input your Hugging Face and Groq tokens securely
-Transalate_token = getpass("Enter Hugging Face Translation Token: ")
-Image_Token = getpass("Enter Hugging Face Image Generation Token: ")
-Content_Token = getpass("Enter Groq Content Generation Token: ")
-Image_prompt_token = getpass("Enter Groq Prompt Generation Token: ")
+# Hugging Face token from secrets
+HF_TOKEN = st.secrets["HF_TOKEN"]
 
-# API Keys for GPT and Gemini (replace with your actual keys)
-openai.api_key = getpass("Enter OpenAI API Key: ")
-# gemini_token = getpass("Enter Gemini API Key: ")  # Placeholder, you will need API access
+# Streamlit page settings
+st.set_page_config(page_title="Tamil to Image Generator", layout="centered")
 
-# API Headers
-Translate = {"Authorization": f"Bearer {Transalate_token}"}
-Image_generation = {"Authorization": f"Bearer {Image_Token}"}
-Content_generation = {
-    "Authorization": f"Bearer {Content_Token}",
-    "Content-Type": "application/json"
-}
-Image_Prompt = {
-    "Authorization": f"Bearer {Image_prompt_token}",
-    "Content-Type": "application/json"
-}
+st.title("🧠 Tamil to Image Generator 🎨")
+st.markdown("Enter Tamil text → Translate to English → Generate a creative story → Create an AI Image")
 
-# Translation Model API URL (Tamil to English)
-translation_url = "https://api-inference.huggingface.co/models/facebook/mbart-large-50-many-to-one-mmt"
+# Input Tamil text
+tamil_input = st.text_area("Enter Tamil text", placeholder="உலகின் அழகான கடற்கரை பற்றி சொல்...")
 
-# Text-to-Image Model API URLs
-image_generation_urls = {
-    "black-forest-labs/FLUX.1-schnell": "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell",
-    "CompVis/stable-diffusion-v1-4": "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4",
-    "black-forest-labs/FLUX.1-dev": "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
-}
-
-# Default image generation model
-default_image_model = "black-forest-labs/FLUX.1-schnell"
-
-# Content generation models
-content_models = {
-    "GPT-4 (OpenAI)": "gpt-4",
-    "Gemini-1 (DeepMind)": "gemini-1",
-    "llama-3.1-70b-versatile": "llama-3.1-70b-versatile",
-    "mixtral-8x7b-32768": "mixtral-8x7b-32768"
-}
-
-# Default content generation model
-default_content_model = "GPT-4 (OpenAI)"
-
-# Function to query Hugging Face translation model
-def translate_text(text):
-    payload = {"inputs": text}
-    response = requests.post(translation_url, headers=Translate, json=payload)
-    if response.status_code == 200:
-        result = response.json()
-        translated_text = result[0]['generated_text']
-        return translated_text
-    else:
-        return f"Translation Error {response.status_code}: {response.text}"
-
-# Function to generate content using GPT or Gemini
-def generate_content(english_text, max_tokens, temperature, model):
-    if model == "gpt-4":
-        # Using OpenAI's GPT model
-        response = openai.Completion.create(
-            engine=model,  # GPT model (like gpt-4)
-            prompt=f"Write educational content about {english_text} within {max_tokens} tokens.",
-            max_tokens=max_tokens,
-            temperature=temperature
-        )
-        return response.choices[0].text.strip()
-
-    # elif model == "gemini-1":
-    #     # Placeholder: Add code to call Gemini API here
-    #     # Using the Gemini API (this requires the correct endpoint and token from Google DeepMind)
-    #     # For example, you would create a POST request similar to OpenAI's API.
-    #     url = "https://api.deepmind.com/gemini/v1/generate"
-    #     headers = {
-    #         "Authorization": f"Bearer {gemini_token}",
-    #         "Content-Type": "application/json"
-    #     }
-    #     payload = {
-    #         "model": "gemini-1",
-    #         "input": f"Write educational content about {english_text} within {max_tokens} tokens.",
-    #         "temperature": temperature,
-    #         "max_tokens": max_tokens
-    #     }
-    #     response = requests.post(url, json=payload, headers=headers)
-    #     if response.status_code == 200:
-    #         return response.json()['choices'][0]['text']
-    #     else:
-    #         return f"Gemini Content Generation Error {response.status_code}: {response.text}"
-
-    else:
-        # Default to the Groq API or other models if selected
-        url = "https://api.groq.com/openai/v1/chat/completions"
-        payload = {
-            "model": model,
-            "messages": [
-                {"role": "system", "content": "You are a creative and insightful writer."},
-                {"role": "user", "content": f"Write educational content about {english_text} within {max_tokens} tokens."}
-            ],
-            "max_tokens": max_tokens,
-            "temperature": temperature
-        }
-        response = requests.post(url, json=payload, headers=Content_generation)
-        if response.status_code == 200:
-            result = response.json()
-            return result['choices'][0]['message']['content']
-        else:
-            return f"Content Generation Error: {response.status_code}"
-
-# Function to generate image prompt
-def generate_image_prompt(english_text):
-    payload = {
-        "model": "mixtral-8x7b-32768",
-        "messages": [
-            {"role": "system", "content": "You are a professional Text to image prompt generator."},
-            {"role": "user", "content": f"Create a text to image generation prompt about {english_text} within 30 tokens."}
-        ],
-        "max_tokens": 30
-    }
-    response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=payload, headers=Image_Prompt)
-    if response.status_code == 200:
-        result = response.json()
-        return result['choices'][0]['message']['content']
-    else:
-        return f"Prompt Generation Error: {response.status_code}"
-
-# Function to generate an image from the prompt
-def generate_image(image_prompt, model_url):
-    data = {"inputs": image_prompt}
-    response = requests.post(model_url, headers=Image_generation, json=data)
-    if response.status_code == 200:
-        # Convert the image bytes to a PIL Image
-        image = Image.open(io.BytesIO(response.content))
-        # Save image to a temporary file-like object for Gradio
-        image.save("/tmp/generated_image.png")  # Save the image to a file
-        return "/tmp/generated_image.png"  # Return the path to the image
+if st.button("Generate Image"):
+    if not tamil_input.strip():
+        st.warning("Please enter some Tamil text.")
     else:
-        return f"Image Generation Error {response.status_code}: {response.text}"
+        with st.spinner("🔁 Translating Tamil to English..."):
+            translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ta-en")
+            translated = translator(tamil_input, max_length=100)[0]['translation_text']
 
-# Gradio App
-def fusionmind_app(tamil_input, temperature, max_tokens, content_model, image_model):
-    # Step 1: Translation (Tamil to English)
-    english_text = translate_text(tamil_input)
+        st.success("✅ Translation done")
+        st.markdown(f"**📝 Translated Text:** `{translated}`")
 
-    # Step 2: Generate Educational Content
-    content_output = generate_content(english_text, max_tokens, temperature, content_models[content_model])
+        with st.spinner("🧠 Generating creative English text..."):
+            generator = pipeline("text-generation", model="gpt2")
+            prompt_text = f"Describe this beautifully: {translated}"
+            generated = generator(prompt_text, max_length=80, do_sample=True, top_k=50)[0]['generated_text']
 
-    # Step 3: Generate Image from the prompt
-    image_prompt = generate_image_prompt(english_text)
-    image_data = generate_image(image_prompt, image_generation_urls[image_model])
+        st.success("✅ Text generation done")
+        st.markdown(f"**🎨 Creative Description:** `{generated}`")
 
-    return english_text, content_output, image_data
+        with st.spinner("🖼️ Generating AI Image... (may take 20–30 seconds)"):
+            pipe = StableDiffusionPipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5",
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                use_auth_token=HF_TOKEN
+            ).to("cuda" if torch.cuda.is_available() else "cpu")
 
-# Gradio Interface
-interface = gr.Interface(
-    fn=fusionmind_app,
-    inputs=[
-        gr.Textbox(label="Enter Tamil Text"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
-        gr.Slider(minimum=100, maximum=400, value=200, label="Max Tokens for Content Generation"),
-        gr.Dropdown(list(content_models.keys()), label="Select Content Generation Model", value=default_content_model),
-        gr.Dropdown(list(image_generation_urls.keys()), label="Select Image Generation Model", value=default_image_model)
-    ],
-    outputs=[
-        gr.Textbox(label="Translated English Text"),
-        gr.Textbox(label="Generated Content"),
-        gr.Image(label="Generated Image")  # Display the generated image
-    ],
-    title="TransArt: A Multimodal Application for Vernacular Language Translation and Image Synthesis",
-    description="Translate Tamil to English, generate educational content, and generate related images!"
-)
+            image = pipe(prompt=generated).images[0]
 
-# Launch Gradio App
-interface.launch(debug=True)
+        st.success("✅ Image generated!")
+        st.image(image, caption="🖼️ AI Generated Image", use_column_width=True)