Create app.py

app.py

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+import requests
+import io
+from PIL import Image
+import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load the translation model
+model_name = "Helsinki-NLP/opus-mt-mul-en"
+model = MarianMTModel.from_pretrained(model_name)
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+
+# Function to translate Tamil text to English
+def translate_text(tamil_text):
+    inputs = tokenizer(tamil_text, return_tensors="pt")
+    translated_tokens = model.generate(**inputs)
+    translation = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return translation
+
+# Function to query Gemini API for creative text
+def query_gemini_api(translated_text, gemini_api_key):
+    url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent"
+    headers = {"Content-Type": "application/json"}
+    payload = {
+        "contents": [{"parts": [{"text": translated_text}]}]
+    }
+    response = requests.post(f"{url}?key={gemini_api_key}", headers=headers, json=payload)
+
+    if response.status_code == 200:
+        result = response.json()
+        creative_text = result['candidates'][0]['content']['parts'][0]['text']
+        return creative_text
+    else:
+        return f"Error: {response.status_code} - {response.text}"
+
+# Function to query the Hugging Face API for image generation
+def query_image(payload, huggingface_api_key):
+    API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+    headers = {"Authorization": f"Bearer {huggingface_api_key}"}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.content
+
+# Main function to process input
+def process_input(tamil_input, gemini_api_key, huggingface_api_key):
+    translated_output = translate_text(tamil_input)
+    creative_output = query_gemini_api(translated_output, gemini_api_key)
+    image_bytes = query_image({"inputs": translated_output}, huggingface_api_key)
+    image = Image.open(io.BytesIO(image_bytes))
+    return translated_output, creative_output, image
+
+# Gradio interface
+iface = gr.Interface(
+    fn=process_input,
+    inputs=[
+        gr.Textbox(label="Input Tamil Text"),
+        gr.Textbox(label="Enter your Gemini API Key", type="password"),
+        gr.Textbox(label="Enter your Hugging Face API Key", type="password")
+    ],
+    outputs=[
+        gr.Textbox(label="Translated Text"),
+        gr.Textbox(label="Creative Text"),
+        gr.Image(label="Generated Image")
+    ],
+    title="TRANSART🎨 BY Sakthi",
+    description="Enter Tamil text to translate to English and generate an image based on the translated text."
+)
+
+iface.launch()