Tried to fix the script

model/analyzer.py

@@ -32,147 +32,158 @@ def analyze_script(script):
             device_map="auto"  # Automatically map model to available device
         )
         print("Model loaded successfully")
+
     except Exception as e:
         print(f"An error occurred: {e}")
 
-        # Define trigger categories with their descriptions
-        trigger_categories = {
-            "Violence": {
-                "mapped_name": "Violence",
-                "description": (
-                    "Any act involving physical force or aggression intended to cause harm, injury, or death to a person, animal, or object. "
-                    "Includes direct physical confrontations (e.g., fights, beatings, or assaults), implied violence (e.g., very graphical threats or descriptions of injuries), "
-                    "or large-scale events like wars, riots, or violent protests."
-                )
-            },
-            "Death": {
-                "mapped_name": "Death References",
-                "description": (
-                    "Any mention, implication, or depiction of the loss of life, including direct deaths of characters, including mentions of deceased individuals, "
-                    "or abstract references to mortality (e.g., 'facing the end' or 'gone forever'). This also covers depictions of funerals, mourning, "
-                    "grieving, or any dialogue that centers around death, do not take metaphors into context that don't actually lead to death."
-                )
-            },
-            "Substance Use": {
-                "mapped_name": "Substance Use",
-                "description": (
-                    "Any explicit or implied reference to the consumption, misuse, or abuse of drugs, alcohol, or other intoxicating substances. "
-                    "Includes scenes of drinking, smoking, or drug use, whether recreational or addictive. May also cover references to withdrawal symptoms, "
-                    "rehabilitation, or substance-related paraphernalia (e.g., needles, bottles, pipes)."
-                )
-            },
-            "Gore": {
-                "mapped_name": "Gore",
-                "description": (
-                    "Extremely detailed and graphic depictions of highly severe physical injuries, mutilation, or extreme bodily harm, often accompanied by descriptions of heavy blood, exposed organs, "
-                    "or dismemberment. This includes war scenes with severe casualties, horror scenarios involving grotesque creatures, or medical procedures depicted with excessive detail."
-                )
-            },
-            "Vomit": {
-                "mapped_name": "Vomit",
-                "description": (
-                    "Any reference to the act of vomiting, whether directly described, implied, or depicted in detail. This includes sounds or visual descriptions of the act, "
-                    "mentions of nausea leading to vomiting, or its aftermath (e.g., the presence of vomit, cleaning it up, or characters reacting to it)."
-                )
-            },
-            "Sexual Content": {
-                "mapped_name": "Sexual Content",
-                "description": (
-                    "Any depiction or mention of sexual activity, intimacy, or sexual behavior, ranging from implied scenes to explicit descriptions. "
-                    "This includes romantic encounters, physical descriptions of characters in a sexual context, sexual dialogue, or references to sexual themes (e.g., harassment, innuendos)."
-                )
-            },
-            "Sexual Abuse": {
-                "mapped_name": "Sexual Abuse",
-                "description": (
-                    "Any form of non-consensual sexual act, behavior, or interaction, involving coercion, manipulation, or physical force. "
-                    "This includes incidents of sexual assault, molestation, exploitation, harassment, and any acts where an individual is subjected to sexual acts against their will or without their consent. "
-                    "It also covers discussions or depictions of the aftermath of such abuse, such as trauma, emotional distress, legal proceedings, or therapy. "
-                    "References to inappropriate sexual advances, groping, or any other form of sexual misconduct are also included, as well as the psychological and emotional impact on survivors. "
-                    "Scenes where individuals are placed in sexually compromising situations, even if not directly acted upon, may also fall under this category."
-                )
-            },
-            "Self-Harm": {
-                "mapped_name": "Self-Harm",
-                "description": (
-                    "Any mention or depiction of behaviors where an individual intentionally causes harm to themselves. This includes cutting, burning, or other forms of physical injury, "
-                    "as well as suicidal ideation, suicide attempts, or discussions of self-destructive thoughts and actions. References to scars, bruises, or other lasting signs of self-harm are also included."
-                )
-            },
-            "Gun Use": {
-                "mapped_name": "Gun Use",
-                "description": (
-                    "Any explicit or implied mention of firearms being handled, fired, or used in a threatening manner. This includes scenes of gun violence, references to shootings, "
-                    "gun-related accidents, or the presence of firearms in a tense or dangerous context (e.g., holstered weapons during an argument)."
-                )
-            },
-            "Animal Cruelty": {
-                "mapped_name": "Animal Cruelty",
-                "description": (
-                    "Any act of harm, abuse, or neglect toward animals, whether intentional or accidental. This includes physical abuse (e.g., hitting, injuring, or killing animals), "
-                    "mental or emotional mistreatment (e.g., starvation, isolation), and scenes where animals are subjected to pain or suffering for human entertainment or experimentation."
-                )
-            },
-            "Mental Health Issues": {
-                "mapped_name": "Mental Health Issues",
-                "description": (
-                    "Any reference to mental health struggles, disorders, or psychological distress. This includes mentions of depression, anxiety, PTSD, bipolar disorder, schizophrenia, "
-                    "or other conditions. Scenes depicting therapy sessions, psychiatric treatment, or coping mechanisms (e.g., medication, journaling) are also included. May cover subtle hints "
-                    "like a character expressing feelings of worthlessness, hopelessness, or detachment from reality."
-                )
-            }
+    # Define trigger categories with their descriptions
+    trigger_categories = {
+        "Violence": {
+            "mapped_name": "Violence",
+            "description": (
+                "Any act involving physical force or aggression intended to cause harm, injury, or death to a person, animal, or object. "
+                "Includes direct physical confrontations (e.g., fights, beatings, or assaults), implied violence (e.g., very graphical threats or descriptions of injuries), "
+                "or large-scale events like wars, riots, or violent protests."
+            )
+        },
+        "Death": {
+            "mapped_name": "Death References",
+            "description": (
+                "Any mention, implication, or depiction of the loss of life, including direct deaths of characters, including mentions of deceased individuals, "
+                "or abstract references to mortality (e.g., 'facing the end' or 'gone forever'). This also covers depictions of funerals, mourning, "
+                "grieving, or any dialogue that centers around death, do not take metaphors into context that don't actually lead to death."
+            )
+        },
+        "Substance Use": {
+            "mapped_name": "Substance Use",
+            "description": (
+                "Any explicit or implied reference to the consumption, misuse, or abuse of drugs, alcohol, or other intoxicating substances. "
+                "Includes scenes of drinking, smoking, or drug use, whether recreational or addictive. May also cover references to withdrawal symptoms, "
+                "rehabilitation, or substance-related paraphernalia (e.g., needles, bottles, pipes)."
+            )
+        },
+        "Gore": {
+            "mapped_name": "Gore",
+            "description": (
+                "Extremely detailed and graphic depictions of highly severe physical injuries, mutilation, or extreme bodily harm, often accompanied by descriptions of heavy blood, exposed organs, "
+                "or dismemberment. This includes war scenes with severe casualties, horror scenarios involving grotesque creatures, or medical procedures depicted with excessive detail."
+            )
+        },
+        "Vomit": {
+            "mapped_name": "Vomit",
+            "description": (
+                "Any reference to the act of vomiting, whether directly described, implied, or depicted in detail. This includes sounds or visual descriptions of the act, "
+                "mentions of nausea leading to vomiting, or its aftermath (e.g., the presence of vomit, cleaning it up, or characters reacting to it)."
+            )
+        },
+        "Sexual Content": {
+            "mapped_name": "Sexual Content",
+            "description": (
+                "Any depiction or mention of sexual activity, intimacy, or sexual behavior, ranging from implied scenes to explicit descriptions. "
+                "This includes romantic encounters, physical descriptions of characters in a sexual context, sexual dialogue, or references to sexual themes (e.g., harassment, innuendos)."
+            )
+        },
+        "Sexual Abuse": {
+            "mapped_name": "Sexual Abuse",
+            "description": (
+                "Any form of non-consensual sexual act, behavior, or interaction, involving coercion, manipulation, or physical force. "
+                "This includes incidents of sexual assault, molestation, exploitation, harassment, and any acts where an individual is subjected to sexual acts against their will or without their consent. "
+                "It also covers discussions or depictions of the aftermath of such abuse, such as trauma, emotional distress, legal proceedings, or therapy. "
+                "References to inappropriate sexual advances, groping, or any other form of sexual misconduct are also included, as well as the psychological and emotional impact on survivors. "
+                "Scenes where individuals are placed in sexually compromising situations, even if not directly acted upon, may also fall under this category."
+            )
+        },
+        "Self-Harm": {
+            "mapped_name": "Self-Harm",
+            "description": (
+                "Any mention or depiction of behaviors where an individual intentionally causes harm to themselves. This includes cutting, burning, or other forms of physical injury, "
+                "as well as suicidal ideation, suicide attempts, or discussions of self-destructive thoughts and actions. References to scars, bruises, or other lasting signs of self-harm are also included."
+            )
+        },
+        "Gun Use": {
+            "mapped_name": "Gun Use",
+            "description": (
+                "Any explicit or implied mention of firearms being handled, fired, or used in a threatening manner. This includes scenes of gun violence, references to shootings, "
+                "gun-related accidents, or the presence of firearms in a tense or dangerous context (e.g., holstered weapons during an argument)."
+            )
+        },
+        "Animal Cruelty": {
+            "mapped_name": "Animal Cruelty",
+            "description": (
+                "Any act of harm, abuse, or neglect toward animals, whether intentional or accidental. This includes physical abuse (e.g., hitting, injuring, or killing animals), "
+                "mental or emotional mistreatment (e.g., starvation, isolation), and scenes where animals are subjected to pain or suffering for human entertainment or experimentation."
+            )
+        },
+        "Mental Health Issues": {
+            "mapped_name": "Mental Health Issues",
+            "description": (
+                "Any reference to mental health struggles, disorders, or psychological distress. This includes mentions of depression, anxiety, PTSD, bipolar disorder, schizophrenia, "
+                "or other conditions. Scenes depicting therapy sessions, psychiatric treatment, or coping mechanisms (e.g., medication, journaling) are also included. May cover subtle hints "
+                "like a character expressing feelings of worthlessness, hopelessness, or detachment from reality."
+            )
         }
-
-        print("\nProcessing text...")  # Output indicating the text is being processed
-        chunk_size = 256  # Set the chunk size for text processing
-        overlap = 15  # Overlap between chunks for context preservation
-        script_chunks = [script[i:i + chunk_size] for i in range(0, len(script), chunk_size - overlap)]
-
-        identified_triggers = {}
-
-        for chunk_idx, chunk in enumerate(script_chunks, 1):
-            print(f"\n--- Processing Chunk {chunk_idx}/{len(script_chunks)} ---")
-            for category, info in trigger_categories.items():
-                mapped_name = info["mapped_name"]
-                description = info["description"]
-
-                print(f"\nAnalyzing for {mapped_name}...")
-                prompt = f"""
-                Check this text for any indication of {mapped_name} ({description}).
-                Be sensitive to subtle references or implications, make sure the text is not metaphorical.
-                Respond concisely with: YES, NO, or MAYBE.
-                Text: {chunk}
-                Answer:
-                """
-
-                print(f"Sending prompt to model...")  # Indicate that prompt is being sent to the model
-                inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)  # Tokenize the prompt
-                inputs = {k: v.to(device) for k, v in inputs.items()}  # Send inputs to the chosen device
-
-                with torch.no_grad():  # Disable gradient calculation for inference
-                    print("Generating response...")  # Indicate that the model is generating a response
-                    outputs = model.generate(
-                        **inputs,
-                        max_new_tokens=3,  # Limit response length
-                        do_sample=True,  # Enable sampling for more diverse output
-                        temperature=0.5,  # Control randomness of the output
-                        top_p=0.9,  # Use nucleus sampling
-                        pad_token_id=tokenizer.eos_token_id  # Pad token ID
-                    )
-                
-                response_text = tokenizer.decode(outputs[0], skip_special_tokens=True).strip().upper()  # Decode and format the response
-                first_word = response_text.split("\n")[-1].split()[0] if response_text else "NO"  # Get the first word of the response
-                print(f"Model response for {mapped_name}: {first_word}")
-
-                # Update identified triggers based on model response
-                if first_word == "YES":
-                   print(f"Detected {mapped_name} in this chunk!")  # Trigger detected
-                   identified_triggers[mapped_name] = identified_triggers.get(mapped_name, 0) + 1
-                elif first_word == "MAYBE":
-                   print(f"Possible {mapped_name} detected, marking for further review.")  # Possible trigger detected
-                   identified_triggers[mapped_name] = identified_triggers.get(mapped_name, 0) + 0.5
-                else:
-                   print(f"No {mapped_name} detected in this chunk.")  # No trigger detected
+    }
+
+    print("\nProcessing text...")  # Output indicating the text is being processed
+    chunk_size = 256  # Set the chunk size for text processing
+    overlap = 15  # Overlap between chunks for context preservation
+    script_chunks = [script[i:i + chunk_size] for i in range(0, len(script), chunk_size - overlap)]
+
+    identified_triggers = {}
+
+    for chunk_idx, chunk in enumerate(script_chunks, 1):
+        print(f"\n--- Processing Chunk {chunk_idx}/{len(script_chunks)} ---")
+        for category, info in trigger_categories.items():
+            mapped_name = info["mapped_name"]
+            description = info["description"]
+
+            print(f"\nAnalyzing for {mapped_name}...")
+            prompt = f"""
+            Check this text for any indication of {mapped_name} ({description}).
+            Be sensitive to subtle references or implications, make sure the text is not metaphorical.
+            Respond concisely with: YES, NO, or MAYBE.
+            Text: {chunk}
+            Answer:
+            """
+
+            print(f"Sending prompt to model...")  # Indicate that prompt is being sent to the model
+            inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)  # Tokenize the prompt
+            inputs = {k: v.to(device) for k, v in inputs.items()}  # Send inputs to the chosen device
+
+            with torch.no_grad():  # Disable gradient calculation for inference
+                print("Generating response...")  # Indicate that the model is generating a response
+                outputs = model.generate(
+                    **inputs,
+                    max_new_tokens=3,  # Limit response length
+                    do_sample=True,  # Enable sampling for more diverse output
+                    temperature=0.5,  # Control randomness of the output
+                    top_p=0.9,  # Use nucleus sampling
+                    pad_token_id=tokenizer.eos_token_id  # Pad token ID
+                )
+            
+            response_text = tokenizer.decode(outputs[0], skip_special_tokens=True).strip().upper()  # Decode and format the response
+            first_word = response_text.split("\n")[-1].split()[0] if response_text else "NO"  # Get the first word of the response
+            print(f"Model response for {mapped_name}: {first_word}")
+
+            # Update identified triggers based on model response
+            if first_word == "YES":
+                print(f"Detected {mapped_name} in this chunk!")  # Trigger detected
+                identified_triggers[mapped_name] = identified_triggers.get(mapped_name, 0) + 1
+            elif first_word == "MAYBE":
+                print(f"Possible {mapped_name} detected, marking for further review.")  # Possible trigger detected
+                identified_triggers[mapped_name] = identified_triggers.get(mapped_name, 0) + 0.5
+            else:
+                print(f"No {mapped_name} detected in this chunk.")  # No trigger detected
+
+    print("\n=== Analysis Complete ===")  # Indicate that analysis is complete
+    print("Final Results:") 
+    final_triggers = []  # List to store final triggers
+
+    # Filter and output the final trigger results
+    for mapped_name, count in identified_triggers.items():
+        if count > 0.5:
+            final_triggers.append(mapped_name)
+        print(f"- {mapped_name}: found in {count} chunks")
 
         print("\n=== Analysis Complete ===")  # Indicate that analysis is complete
         print("Final Results:") 
@@ -189,30 +200,9 @@ def analyze_script(script):
 
         return final_triggers
 
-def analyze_content(script):
-    triggers = analyze_script(script)
-
-    if isinstance(triggers, list) and triggers != ["None"]:
-        result = {
-            "detected_triggers": triggers,
-            "confidence": "High - Content detected",
-            "model": "Llama-3.2-1B",
-            "analysis_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        }
-    else:
-        result = {
-            "detected_triggers": ["None"],
-            "confidence": "High - No concerning content detected",
-            "model": "Llama-3.2-1B",
-            "analysis_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        }
-
-    print("\nFinal Result Dictionary:", result)
-    return result
-
 # Define the Gradio interface
 import gradio as gr
-from model import analyze_script  # Import the function to analyze the content
+from datetime import datetime
 
 def analyze_content(script):
     # Perform the analysis on the input script using the analyze_script function