Update app.py

app.py

@@ -65,164 +65,19 @@
 # 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,
+    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
@@ -267,25 +122,28 @@ def is_caption_safe(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
+        # 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 keyword check
+
+    # 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
 # ----------------------
@@ -295,22 +153,22 @@ def generate_caption_translate_speak(image, target_lang):
     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
 
 # ----------------------
@@ -321,11 +179,11 @@ def vqa_answer(image, question):
     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
+
+    # Run safety filter on answers too
     if not is_caption_safe(answer):
         return "⚠️ Warning: Unsafe or inappropriate content detected!"
-    
+
     return answer
 
 # ----------------------
@@ -333,24 +191,24 @@ def vqa_answer(image, question):
 # ----------------------
 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])
-    
+        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)
+        ans_out = gr.Textbox(label="Answer")
+        btn2 = gr.Button("Ask")
+        btn2.click(vqa_answer, inputs=[img_vqa, q_in], outputs=ans_out)
 
 demo.launch()
 
@@ -358,6 +216,148 @@ 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()
+
+
+
+
+