Update app.py

app.py

@@ -66,6 +66,158 @@
 # 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, 
@@ -73,16 +225,12 @@ from transformers import (
     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
+import numpy as np
+import soundfile as sf
 
 # ----------------------
 # Device setup
@@ -114,37 +262,46 @@ moderation_model = pipeline("text-classification", model="unitary/toxic-bert")
 
 print("✅ All models loaded!")
 
+
+# ----------------------
+# Utility: Generate a Beep Sound
+# ----------------------
+def make_beep_sound(duration=0.5, freq=1000):
+    """Generate a short beep tone and save as temporary .wav file."""
+    samplerate = 44100
+    t = np.linspace(0, duration, int(samplerate * duration), endpoint=False)
+    wave = 0.5 * np.sin(2 * np.pi * freq * t)
+    tmp_beep = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+    sf.write(tmp_beep.name, wave, samplerate)
+    return tmp_beep.name
+
+
 # ----------------------
 # 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"
+        "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
 # ----------------------
@@ -157,7 +314,8 @@ def generate_caption_translate_speak(image, target_lang):
 
     # Step 1.5: Safety Check
     if not is_caption_safe(english_caption):
-        return "⚠️ Warning: Unsafe or inappropriate content detected!", "", None
+        beep = make_beep_sound()
+        return "⚠️ Warning: Unsafe or inappropriate content detected!", "", beep
 
     # Step 2: Translate
     if target_lang in translation_models:
@@ -172,6 +330,7 @@ def generate_caption_translate_speak(image, target_lang):
 
     return english_caption, translated, tmp_file.name
 
+
 # ----------------------
 # VQA
 # ----------------------
@@ -181,17 +340,18 @@ def vqa_answer(image, question):
         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!"
+        beep = make_beep_sound()
+        return "⚠️ Warning: Unsafe or inappropriate content detected!", beep
+
+    return answer, None
 
-    return answer
 
 # ----------------------
 # Gradio UI
 # ----------------------
 with gr.Blocks(title="BLIP Vision App") as demo:
-    gr.Markdown("## 🖼️ BLIP: Image Captioning + Translation + Speech + VQA (with Safety Filter)")
+    gr.Markdown("## 🖼️ BLIP: Image Captioning + Translation + Speech + VQA (with Safety Filter + Beep Alert)")
 
     with gr.Tab("Caption + Translate + Speak"):
         with gr.Row():
@@ -199,7 +359,7 @@ with gr.Blocks(title="BLIP Vision App") as demo:
             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")
+        audio_out = gr.Audio(label="Audio Output", 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])
 
@@ -208,10 +368,12 @@ with gr.Blocks(title="BLIP Vision App") as demo:
             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")
+        beep_out = gr.Audio(label="Alert Sound", type="filepath")
         btn2 = gr.Button("Ask")
-        btn2.click(vqa_answer, inputs=[img_vqa, q_in], outputs=ans_out)
+        btn2.click(vqa_answer, inputs=[img_vqa, q_in], outputs=[ans_out, beep_out])
 
 demo.launch()
 
+