app.py

# import gradio as gr
# from transformers import BlipProcessor, BlipForConditionalGeneration
# from gtts import gTTS
# import io
# from PIL import Image

# # -------------------------------
# # Load BLIP-base model (lighter version)
# # -------------------------------
# processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
# model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")

# # -------------------------------
# # Generate caption function
# # -------------------------------
# # def generate_caption_tts(image):
# #     caption = generate_caption(model, processor, image)
# #     audio_file = text_to_audio_file(caption)
# #     return caption, audio_file  # return file path, not BytesIO


# # -------------------------------
# # Convert text to speech using gTTS
# # -------------------------------
# import tempfile
# import pyttsx3

# def text_to_audio_file(text):
#     # Create a temporary file
#     tmp_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
#     tmp_path = tmp_file.name
#     tmp_file.close()

#     engine = pyttsx3.init()
#     engine.save_to_file(text, tmp_path)
#     engine.runAndWait()

#     return tmp_path

# def generate_caption_from_image(model, processor, image):
#     # image: PIL.Image
#     inputs = processor(images=image, return_tensors="pt")
#     out = model.generate(**inputs)
#     caption = processor.decode(out[0], skip_special_tokens=True)
#     return caption
# # -------------------------------
# # Gradio interface: Caption + Audio
# # -------------------------------
# def generate_caption_tts(image):
#     caption = generate_caption_from_image(model, processor, image)  # uses global model/processor
#     # audio_file = text_to_audio_file(caption)
#     return caption 



# interface = gr.Interface(
#     fn=generate_caption_tts,
#     inputs=gr.Image(type="numpy"),
#     outputs=[gr.Textbox(label="Generated Caption")],
#     title="Image Captioning for Visually Impaired",
#     description="Upload an image, get a caption and audio description."
# )


# interface.launch()
# # demo.launch(share=True)

import gradio as gr
from transformers import (
    BlipProcessor, 
    BlipForConditionalGeneration, 
    BlipForQuestionAnswering, 
    pipeline
)
moderation_model = pipeline(
    "text-classification",
    model="Vrandan/Comment-Moderation",
    return_all_scores=True
)

from PIL import Image
import torch
from gtts import gTTS
import tempfile

# ----------------------
# Device setup
# ----------------------
device = "cuda" if torch.cuda.is_available() else "cpu"

# ----------------------
# Load Models Once
# ----------------------
print("🔄 Loading models...")

# Captioning
caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)

# VQA
vqa_processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
vqa_model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(device)

# Translation
translation_models = {
    "Hindi": pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi"),
    "French": pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr"),
    "Spanish": pipeline("translation", model="Helsinki-NLP/opus-mt-en-es"),
}

# Safety Moderation Pipeline
moderation_model = pipeline("text-classification", model="unitary/toxic-bert")

print("✅ All models loaded!")

# ----------------------
# Safety Filter Function
# ----------------------
def is_caption_safe(caption):
    try:
        votes = moderation_model(caption)
        # If return_all_scores=True, it's [[{label, score}, ...]]
        if isinstance(votes, list) and isinstance(votes[0], list):
            votes = votes[0]
        # Now safe to loop
        for item in votes:
            if isinstance(item, dict) and item.get("label") in ["V", "V2"] and item.get("score", 0) > 0.5:
                return False
    except Exception as e:
        print("⚠️ Moderation failed:", e)

    # Fallback keywords
    unsafe_keywords = [
    "gun", "blood", "skull", "kill", "corpse", "gore", "knife", "weapon",
    "fire", "murder", "dead", "death", "suicide", "bomb", "explosion",
    "terrorist", "assault", "stab", "shoot", "pistol", "rifle", "shotgun",
    "grenade", "horror", "beheaded", "torture", "hostage", "rape",
    "war", "massacre", "chainsaw", "poison", "strangle", "hang", "drown"
    ]
    if any(word in caption.lower() for word in unsafe_keywords):
        return False
    return True




# ----------------------
# Caption + Translate + Speak
# ----------------------
def generate_caption_translate_speak(image, target_lang):
    # Step 1: Caption
    inputs = caption_processor(images=image, return_tensors="pt").to(device)
    with torch.no_grad():
        out = caption_model.generate(**inputs, max_new_tokens=50)
    english_caption = caption_processor.decode(out[0], skip_special_tokens=True)

    # Step 1.5: Safety Check
    if not is_caption_safe(english_caption):
        return "⚠️ Warning: Unsafe or inappropriate content detected!", "", None

    # Step 2: Translate
    if target_lang in translation_models:
        translated = translation_models[target_lang](english_caption)[0]['translation_text']
    else:
        translated = "Translation not available"

    # Step 3: Generate Speech (English caption for now)
    tts = gTTS(english_caption, lang="en")
    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
    tts.save(tmp_file.name)

    return english_caption, translated, tmp_file.name

# ----------------------
# VQA
# ----------------------
def vqa_answer(image, question):
    inputs = vqa_processor(image, question, return_tensors="pt").to(device)
    with torch.no_grad():
        out = vqa_model.generate(**inputs, max_new_tokens=50)
    answer = vqa_processor.decode(out[0], skip_special_tokens=True)

    # Run safety filter on answers too
    if not is_caption_safe(answer):
        return "⚠️ Warning: Unsafe or inappropriate content detected!"

    return answer

# ----------------------
# Gradio UI
# ----------------------
with gr.Blocks(title="BLIP Vision App") as demo:
    gr.Markdown("## 🖼️ BLIP: Image Captioning + Translation + Speech + VQA (with Safety Filter)")

    with gr.Tab("Caption + Translate + Speak"):
        with gr.Row():
            img_in = gr.Image(type="pil", label="Upload Image")
            lang_in = gr.Dropdown(["Hindi", "French", "Spanish"], label="Translate To", value="Hindi")
        eng_out = gr.Textbox(label="English Caption")
        trans_out = gr.Textbox(label="Translated Caption")
        audio_out = gr.Audio(label="Spoken Caption", type="filepath")
        btn1 = gr.Button("Generate Caption, Translate & Speak")
        btn1.click(generate_caption_translate_speak, inputs=[img_in, lang_in], outputs=[eng_out, trans_out, audio_out])

    with gr.Tab("Visual Question Answering (VQA)"):
        with gr.Row():
            img_vqa = gr.Image(type="pil", label="Upload Image")
            q_in = gr.Textbox(label="Ask a Question about the Image")
        ans_out = gr.Textbox(label="Answer")
        btn2 = gr.Button("Ask")
        btn2.click(vqa_answer, inputs=[img_vqa, q_in], outputs=ans_out)

demo.launch()





# import gradio as gr
# from transformers import (
#     BlipProcessor,
#     BlipForConditionalGeneration,
#     BlipForQuestionAnswering,
#     pipeline
# )
# from PIL import Image
# import torch
# from gtts import gTTS
# import tempfile

# # ----------------------
# # Device setup
# # ----------------------
# device = "cuda" if torch.cuda.is_available() else "cpu"

# # ----------------------
# # Load Models Once
# # ----------------------
# print("🔄 Loading models...")

# # Captioning
# caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
# caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)

# # VQA
# vqa_processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
# vqa_model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(device)

# # Translation
# translation_models = {
#     "Hindi": pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi"),
#     "French": pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr"),
#     "Spanish": pipeline("translation", model="Helsinki-NLP/opus-mt-en-es"),
# }

# # Safety Moderation Pipeline
# moderation_model = pipeline("text-classification", model="unitary/toxic-bert")

# print("✅ All models loaded!")

# # ----------------------
# # Safety Filter Function
# # ----------------------
# def is_caption_safe(caption):
#     try:
#         votes = moderation_model(caption)
#         # If return_all_scores=True, it's [[{label, score}, ...]]
#         if isinstance(votes, list) and isinstance(votes[0], list):
#             votes = votes[0]
#         # Loop through scores
#         for item in votes:
#             if isinstance(item, dict) and item.get("label") in ["V", "V2"] and item.get("score", 0) > 0.5:
#                 return False
#     except Exception as e:
#         print("⚠️ Moderation failed:", e)
    
#     # Fallback keyword check
#     unsafe_keywords = [
#         "gun", "blood", "skull", "kill", "corpse", "gore", "knife", "weapon", "fire",
#         "murder", "dead", "death", "suicide", "bomb", "explosion", "terrorist", "assault",
#         "stab", "shoot", "pistol", "rifle", "shotgun", "grenade", "horror", "beheaded",
#         "torture", "hostage", "rape", "war", "massacre", "chainsaw", "poison", "strangle",
#         "hang", "drown"
#     ]
#     if any(word in caption.lower() for word in unsafe_keywords):
#         return False
#     return True

# # ----------------------
# # Caption + Translate + Speak
# # ----------------------
# def generate_caption_translate_speak(image, target_lang):
#     # Step 1: Caption
#     inputs = caption_processor(images=image, return_tensors="pt").to(device)
#     with torch.no_grad():
#         out = caption_model.generate(**inputs, max_new_tokens=50)
#     english_caption = caption_processor.decode(out[0], skip_special_tokens=True)
    
#     # Step 1.5: Safety Check
#     if not is_caption_safe(english_caption):
#         return "⚠️ Warning: Unsafe or inappropriate content detected!", "", None
    
#     # Step 2: Translate
#     if target_lang in translation_models:
#         translated = translation_models[target_lang](english_caption)[0]['translation_text']
#     else:
#         translated = "Translation not available"
    
#     # Step 3: Generate Speech (English caption for now)
#     tts = gTTS(english_caption, lang="en")
#     tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
#     tts.save(tmp_file.name)
    
#     return english_caption, translated, tmp_file.name

# # ----------------------
# # VQA
# # ----------------------
# def vqa_answer(image, question):
#     inputs = vqa_processor(image, question, return_tensors="pt").to(device)
#     with torch.no_grad():
#         out = vqa_model.generate(**inputs, max_new_tokens=50)
#     answer = vqa_processor.decode(out[0], skip_special_tokens=True)
    
#     # Safety filter
#     if not is_caption_safe(answer):
#         return "⚠️ Warning: Unsafe or inappropriate content detected!"
    
#     return answer

# # ----------------------
# # Gradio UI
# # ----------------------
# with gr.Blocks(title="BLIP Vision App") as demo:
#     gr.Markdown("## 🖼️ BLIP: Image Captioning + Translation + Speech + VQA (with Safety Filter)")
    
#     with gr.Tab("Caption + Translate + Speak"):
#         with gr.Row():
#             img_in = gr.Image(type="pil", label="Upload Image")
#             lang_in = gr.Dropdown(["Hindi", "French", "Spanish"], label="Translate To", value="Hindi")
#             eng_out = gr.Textbox(label="English Caption")
#             trans_out = gr.Textbox(label="Translated Caption")
#             audio_out = gr.Audio(label="Spoken Caption", type="filepath")
#             btn1 = gr.Button("Generate Caption, Translate & Speak")
#             btn1.click(generate_caption_translate_speak, inputs=[img_in, lang_in], outputs=[eng_out, trans_out, audio_out])
    
#     with gr.Tab("Visual Question Answering (VQA)"):
#         with gr.Row():
#             img_vqa = gr.Image(type="pil", label="Upload Image")
#             q_in = gr.Textbox(label="Ask a Question about the Image")
#             ans_out = gr.Textbox(label="Answer")
#             btn2 = gr.Button("Ask")
#             btn2.click(vqa_answer, inputs=[img_vqa, q_in], outputs=ans_out)

# demo.launch()