Delete app.py

app.py

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-import streamlit as st
-import requests
-import os
-from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
-
-# API keys for other features (optional)
-Image_Token = os.getenv('Image_generation')
-Content_Token = os.getenv('ContentGeneration')
-Image_prompt_token = os.getenv('Prompt_generation')
-
-# API Headers for external services (optional)
-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"
-}
-
-# 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 content generation model
-content_models = {
-    "llama-3.1-70b-versatile": "llama-3.1-70b-versatile",
-    "llama3-8b-8192": "llama3-8b-8192",
-    "gemma2-9b-it": "gemma2-9b-it",
-    "mixtral-8x7b-32768": "mixtral-8x7b-32768"
-}
-
-# Load the translation model and tokenizer locally
-@st.cache_resource
-def load_translation_model():
-    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
-    tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
-    return model, tokenizer
-
-# Function to perform translation locally
-def translate_text_local(text):
-    model, tokenizer = load_translation_model()
-    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
-    translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
-    translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
-    return translated_text
-
-# Function to query Groq content generation model (optional)
-def generate_content(english_text, max_tokens, temperature, model):
-    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:
-        st.error(f"Content Generation Error: {response.status_code}")
-        return None
-
-# Function to generate image prompt (optional)
-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:
-        st.error(f"Prompt Generation Error: {response.status_code}")
-        return None
-
-# Function to generate an image from the prompt (optional)
-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:
-        return response.content
-    else:
-        st.error(f"Image Generation Error {response.status_code}: {response.text}")
-        return None
-
-# User Guide Section
-def show_user_guide():
-    st.title("FusionMind User Guide")
-    st.write("""
-### Welcome to the FusionMind User Guide!
-
-### How to use this app:
-
-1. **Input Tamil Text**:
-   - You can either select one of the suggested Tamil phrases or input your own text. The app primarily focuses on Tamil inputs, but it supports a wide range of other languages as well (see the list below).
-
-2. **Generate Translations**:
-   - Once you've input your text, the app will automatically translate it to English. The translation model is a **many-to-one model**, meaning it can take input from various languages and translate it into English.
-
-3. **Generate Educational Content**:
-   - After translating the text into English, the app will generate **educational content** based on the translated input. You can adjust the creativity of the content generation using the temperature slider, and control the length of the output with the token limit setting.
-
-4. **Generate Images**:
-   - In addition to generating content, the app can also generate an **image** related to the translated content. You don’t need to worry about creating complex image prompts—FusionMind includes an automatic **image prompt generator** that will convert your input into a well-defined image prompt, ensuring better image generation results.
-
----
-
-### Features:
-
-- **Multilingual Translation**:
-   - FusionMind supports a **many-to-one translation model**, so you can input text in a wide variety of languages, not just Tamil. Below are the supported languages:
-
-     - **Arabic (ar_AR)**, **Czech (cs_CZ)**, **German (de_DE)**, **English (en_XX)**, **Spanish (es_XX)**, **Estonian (et_EE)**, **Finnish (fi_FI)**, **French (fr_XX)**, **Gujarati (gu_IN)**, **Hindi (hi_IN)**, **Italian (it_IT)**, **Japanese (ja_XX)**, **Kazakh (kk_KZ)**, **Korean (ko_KR)**, **Lithuanian (lt_LT)**, **Latvian (lv_LV)**, **Burmese (my_MM)**, **Nepali (ne_NP)**, **Dutch (nl_XX)**, **Romanian (ro_RO)**, **Russian (ru_RU)**, **Sinhala (si_LK)**, **Turkish (tr_TR)**, **Vietnamese (vi_VN)**, **Chinese (zh_CN)**, **Afrikaans (af_ZA)**, **Azerbaijani (az_AZ)**, **Bengali (bn_IN)**, **Persian (fa_IR)**, **Hebrew (he_IL)**, **Croatian (hr_HR)**, **Indonesian (id_ID)**, **Georgian (ka_GE)**, **Khmer (km_KH)**, **Macedonian (mk_MK)**, **Malayalam (ml_IN)**, **Mongolian (mn_MN)**, **Marathi (mr_IN)**, **Polish (pl_PL)**, **Pashto (ps_AF)**, **Portuguese (pt_XX)**, **Swedish (sv_SE)**, **Swahili (sw_KE)**, **Tamil (ta_IN)**, **Telugu (te_IN)**, **Thai (th_TH)**, **Tagalog (tl_XX)**, **Ukrainian (uk_UA)**, **Urdu (ur_PK)**, **Xhosa (xh_ZA)**, **Galician (gl_ES)**, **Slovene (sl_SI)**.
-
-- **Temperature Adjustment**:
-   - You can adjust the **temperature** of the content generation. A **higher temperature** makes the content more creative and varied, while a **lower temperature** generates more focused and deterministic responses.
-
-- **Token Limit**:
-   - Set the **maximum number of tokens** for content generation. This allows you to control the length of the generated educational content.
-
-- **Auto-Generated Image Prompts**:
-   - One of the unique features of FusionMind is the **auto-generated image prompts**. Even if you're not experienced in creating detailed prompts for image generation, the app will take care of this for you. It automatically converts the translated text or content into a well-defined prompt that produces more accurate and high-quality images.
-
----
-
-Enjoy the multimodal experience with **FusionMind** and explore its powerful translation, content generation, and image generation features!
-    """)
-
-# Main Streamlit app
-def main():
-    # Sidebar Menu
-    st.sidebar.title("FusionMind Options")
-    page = st.sidebar.radio("Select a page:", ["Main App", "User Guide"])
-
-    if page == "User Guide":
-        show_user_guide()
-        return
-
-    st.title("🅰️ℹ️ FusionMind ➡️ Multimodal")
-
-    # Sidebar for temperature, token adjustment, and model selection
-    st.sidebar.header("Settings")
-    temperature = st.sidebar.slider("Select Temperature", 0.1, 1.0, 0.7)
-    max_tokens = st.sidebar.slider("Max Tokens for Content Generation", 100, 400, 200)
-
-    # Content generation model selection
-    content_model = st.sidebar.selectbox("Select Content Generation Model", list(content_models.keys()), index=0)
-
-    # Image generation model selection
-    image_model = st.sidebar.selectbox("Select Image Generation Model", list(image_generation_urls.keys()), index=0)
-
-    # Suggested inputs
-    st.write("## Suggested Inputs")
-    suggestions = ["தரவு அறிவியல்", "உளவியல்", "ராக்கெட் எப்படி வேலை செய்கிறது"]
-    selected_suggestion = st.selectbox("Select a suggestion or enter your own:", [""] + suggestions)
-
-    # Input box for user
-    tamil_input = st.text_input("Enter Tamil text (or select a suggestion):", selected_suggestion)
-
-    if st.button("Generate"):
-        # Step 1: Translation (Tamil to English)
-        if tamil_input:
-            st.write("### Translated English Text:")
-            english_text = translate_text_local(tamil_input)
-            if english_text:
-                st.success(english_text)
-
-                # Step 2: Generate Educational Content
-                st.write("### Generated Content:")
-                with st.spinner('Generating content...'):
-                    content_output = generate_content(english_text, max_tokens, temperature, content_models[content_model])
-                    if content_output:
-                        st.success(content_output)
-
-                # Step 3: Generate Image from the prompt (optional)
-                st.write("### Generated Image:")
-                with st.spinner('Generating image...'):
-                    image_prompt = generate_image_prompt(english_text)
-                    image_data = generate_image(image_prompt, image_generation_urls[image_model])
-                    if image_data:
-                        st.image(image_data, caption="Generated Image")
-
-if __name__ == "__main__":
-    main()