Create app.py

app.py

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+import os
+import requests
+import streamlit as st
+from PIL import Image
+from io import BytesIO
+from transformers import MBartForConditionalGeneration, MBart50Tokenizer
+import time
+
+# Fetch the API keys from Hugging Face Secrets
+HUGGINGFACE_TOKEN = os.getenv("Hugging_face_token")
+GROQ_API_KEY = os.getenv("Groq_api")
+
+# Hugging Face API endpoint
+HF_API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+hf_headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
+
+# Groq API endpoint
+groq_url = "https://api.groq.com/openai/v1/chat/completions"
+groq_headers = {
+    "Authorization": f"Bearer {GROQ_API_KEY}",
+    "Content-Type": "application/json"
+}
+
+# Function to query Hugging Face model for image generation
+def query_huggingface(payload):
+    response = requests.post(HF_API_URL, headers=hf_headers, json=payload)
+    if response.status_code != 200:
+        st.error(f"Error: {response.status_code} - {response.text}")
+        return None
+    return response.content
+
+# Function to generate text using Groq API
+def generate_response(prompt):
+    payload = {
+        "model": "mixtral-8x7b-32768",
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ],
+        "max_tokens": 100,
+        "temperature": 0.7
+    }
+    response = requests.post(groq_url, json=payload, headers=groq_headers)
+    if response.status_code == 200:
+        result = response.json()
+        return result['choices'][0]['message']['content']
+    else:
+        st.error(f"Error: {response.status_code} - {response.text}")
+        return None
+
+# Function to translate Tamil to English using MBart model
+def translate_tamil_to_english(tamil_text):
+    model_name = "facebook/mbart-large-50-many-to-one-mmt"
+    model = MBartForConditionalGeneration.from_pretrained(model_name)
+    tokenizer = MBart50Tokenizer.from_pretrained(model_name, src_lang="ta_IN")
+
+    inputs = tokenizer(tamil_text, return_tensors="pt")
+    translated = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
+    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+    return translated_text
+
+# Main function to generate text and image
+def generate_image_and_text(user_input):
+    with st.spinner("Generating results..."):
+        time.sleep(2)  # Simulate some processing time
+
+        # Translate Tamil to English
+        english_input = translate_tamil_to_english(user_input)
+        if not english_input:
+            st.error("Error in translation.")
+            return
+
+        # Generate text description (100 tokens) and image prompt (30 tokens) using Groq API
+        full_text_description = generate_response(english_input)
+        if not full_text_description:
+            st.error("Error in text generation.")
+            return
+
+        # Create image prompt based on the full text description
+        image_prompt = generate_response(f"Create a concise image prompt from the following text: {full_text_description}")
+        if not image_prompt:
+            st.error("Error in generating image prompt.")
+            return
+
+        # Request an image based on the generated image prompt
+        image_data = query_huggingface({"inputs": image_prompt})
+        if not image_data:
+            st.error("Error in image generation.")
+            return
+
+        # Display the results
+        st.markdown("### Translated English Text:")
+        st.write(english_input)
+
+        st.markdown("### Generated Text Response:")
+        st.write(full_text_description)
+
+        try:
+            # Load and display the image
+            image = Image.open(BytesIO(image_data))
+            st.image(image, caption="Generated Image", use_column_width=True)
+        except Exception as e:
+            st.error(f"Failed to display image: {e}")
+
+# Streamlit interface
+st.title("Multi-Modal Generator (Tamil to English)")
+st.write("Enter a prompt in Tamil to generate both text and an image.")
+
+# Input field for Tamil text
+user_input = st.text_input("Enter Tamil text here:")
+
+# Generate results when button is clicked
+if st.button("Generate"):
+    if user_input:
+        generate_image_and_text(user_input)
+    else:
+        st.error("Please enter a Tamil text.")