something broke, trying model without the loading bar.

gradio_app.py

@@ -8,14 +8,12 @@ custom_css = """
 * {
     font-family: 'Roboto', sans-serif;
 }
-
 .gradio-container {
     background: #121212 !important;
     color: #fff !important;
     overflow: hidden;
     transition: background 0.5s ease;
 }
-
 .treat-title {
     text-align: center;
     padding: 40px;
@@ -25,7 +23,6 @@ custom_css = """
     box-shadow: 0 4px 8px rgba(0, 0, 0, 0.3);
     animation: slideInFromTop 1s ease-out;
 }
-
 .treat-title h1 {
     font-size: 5em;
     color: #4f46e5;
@@ -33,18 +30,15 @@ custom_css = """
     font-weight: bold;
     animation: fadeInText 1.5s ease-out;
 }
-
 .treat-title p {
     font-size: 1.3em;
     color: #4f46e5;
     animation: fadeInText 1.5s ease-out 0.5s;
 }
-
 .highlight {
     color: #4f46e5;
     font-weight: bold;
 }
-
 .content-area, .results-area {
     background: rgba(33, 33, 33, 0.9) !important;
     border-radius: 15px !important;
@@ -54,7 +48,6 @@ custom_css = """
     opacity: 0;
     animation: fadeInUp 1s forwards;
 }
-
 .gradio-textbox textarea {
     background-color: #333 !important;
     color: #fff !important;
@@ -64,11 +57,9 @@ custom_css = """
     font-size: 1.1em !important;
     transition: border-color 0.3s ease;
 }
-
 .gradio-textbox textarea:focus {
     border-color: #4f46e5 !important;
 }
-
 .gradio-button {
     background-color: #4f46e5 !important;
     color: white !important;
@@ -79,29 +70,24 @@ custom_css = """
     transition: transform 0.3s ease, background-color 0.3s ease;
     margin: 20px 0 !important;
 }
-
 .gradio-button:hover {
     transform: scale(1.1) !important;
     background-color: #5749d3 !important;
 }
-
 .gradio-button:active {
     transform: scale(0.98) !important;
     background-color: #4b40bb !important;
 }
-
 label {
     color: #ccc !important;
     font-weight: 500 !important;
     margin-bottom: 10px !important;
 }
-
 .center-row {
     display: flex;
     justify-content: center;
     align-items: center;
 }
-
 .footer {
     text-align: center;
     margin-top: 40px;
@@ -110,45 +96,55 @@ label {
     opacity: 0;
     animation: fadeInUp 1s forwards 1.5s;
 }
-
 .footer p {
     color: #4f46e5;
 }
-
 @keyframes slideInFromTop {
     0% { transform: translateY(-50px); opacity: 0; }
     100% { transform: translateY(0); opacity: 1; }
 }
-
 @keyframes fadeInText {
     0% { opacity: 0; }
     100% { opacity: 1; }
 }
-
 @keyframes fadeInUp {
     0% { opacity: 0; transform: translateY(30px); }
     100% { opacity: 1; transform: translateY(0); }
 }
 """
 
-async def analyze_with_loading(text, progress=gr.Progress()):
+def analyze_with_loading(text, progress=gr.Progress()):
     """
-    Asynchronous wrapper for analyze_content that properly tracks progress
+    Synchronous wrapper for the async analyze_content function
     """
+    # Initialize progress
+    progress(0, desc="Starting analysis...")
+    
+    # Initial setup phase
+    for i in range(30):
+        time.sleep(0.02)  # Reduced sleep time
+        progress((i + 1) / 100)
+    
+    # Perform analysis
+    progress(0.3, desc="Processing text...")
     try:
-        # Call analyze_content directly with the progress object
-        result = await analyze_content(text, progress)
-        
-        # Format the results
-        triggers = result["detected_triggers"]
-        if triggers == ["None"]:
-            return "✓ No concerns detected in the content."
-        else:
-            trigger_list = "\n".join([f"• {trigger}" for trigger in triggers])
-            return f"⚠ Triggers Detected:\n{trigger_list}"
-            
+        # Use asyncio.run to handle the async function call
+        result = asyncio.run(analyze_content(text))
     except Exception as e:
         return f"Error during analysis: {str(e)}"
+    
+    # Final processing
+    for i in range(70, 100):
+        time.sleep(0.02)  # Reduced sleep time
+        progress((i + 1) / 100)
+    
+    # Format the results
+    triggers = result["detected_triggers"]
+    if triggers == ["None"]:
+        return "✓ No triggers detected in the content."
+    else:
+        trigger_list = "\n".join([f"• {trigger}" for trigger in triggers])
+        return f"⚠ Triggers Detected:\n{trigger_list}"
 
 # Create the Gradio interface
 with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as iface:
@@ -206,6 +202,7 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as iface:
     """)
 
 if __name__ == "__main__":
+    # Launch without the 'ssr' argument
     iface.launch(
         share=False,
         debug=True,

model/analyzer.py

@@ -122,7 +122,7 @@ class ContentAnalyzer:
             )
 
             if progress:
-                progress(0.15, "Loading model...")
+                progress(0.3, "Loading model...")
             
             self.model = AutoModelForCausalLM.from_pretrained(
                 "meta-llama/Llama-3.2-1B",
@@ -132,7 +132,7 @@ class ContentAnalyzer:
             )
 
             if progress:
-                progress(0.2, "Model loaded successfully")
+                progress(0.5, "Model loaded successfully")
             
             logger.info(f"Model loaded successfully on {self.device}")
         except Exception as e:
@@ -147,6 +147,7 @@ class ContentAnalyzer:
         
         while start < text_len:
             end = min(start + chunk_size, text_len)
+            # Find the last period or newline in the chunk to avoid cutting sentences
             if end < text_len:
                 last_period = max(
                     text.rfind('. ', start, end),
@@ -164,6 +165,7 @@ class ContentAnalyzer:
         """Process model response and return a confidence score."""
         response = response_text.strip().upper()
         
+        # Check for explicit YES/NO/MAYBE
         if "YES" in response:
             evidence_words = ["CLEAR", "DEFINITELY", "EXPLICIT", "STRONG"]
             return 1.0 if any(word in response for word in evidence_words) else 0.8
@@ -172,6 +174,7 @@ class ContentAnalyzer:
         elif "NO" in response:
             return 0.0
         
+        # Fallback analysis for unclear responses
         positive_indicators = ["PRESENT", "FOUND", "CONTAINS", "SHOWS", "INDICATES"]
         negative_indicators = ["ABSENT", "NONE", "NOTHING", "LACKS"]
         
@@ -191,7 +194,6 @@ class ContentAnalyzer:
     ) -> Dict[str, float]:
         """Analyze a single chunk of text for triggers."""
         chunk_triggers = {}
-        progress_increment = progress_step / len(self.trigger_categories)
         
         for category, info in self.trigger_categories.items():
             mapped_name = info["mapped_name"]
@@ -236,8 +238,8 @@ class ContentAnalyzer:
                     chunk_triggers[mapped_name] = chunk_triggers.get(mapped_name, 0) + confidence
 
                 if progress:
-                    current_progress += progress_increment
-                    progress(min(current_progress, 0.9), f"Analyzing for {mapped_name}...")
+                    current_progress += progress_step
+                    progress(min(current_progress, 0.9), f"Analyzing {mapped_name}...")
 
             except Exception as e:
                 logger.error(f"Error analyzing chunk for {mapped_name}: {str(e)}")
@@ -251,40 +253,28 @@ class ContentAnalyzer:
 
         chunks = self._chunk_text(script)
         trigger_scores = {}
-        
-        # Calculate progress allocation
-        analysis_progress = 0.7  # 70% of progress for analysis
-        progress_per_chunk = analysis_progress / len(chunks)
-        current_progress = 0.2  # Starting after model loading
-        
-        if progress:
-            progress(current_progress, "Beginning content analysis...")
+        progress_step = 0.4 / (len(chunks) * len(self.trigger_categories))
+        current_progress = 0.5
 
-        for i, chunk in enumerate(chunks):
+        for chunk in chunks:
             chunk_triggers = await self.analyze_chunk(
                 chunk,
                 progress,
                 current_progress,
-                progress_per_chunk
+                progress_step
             )
             
             for trigger, score in chunk_triggers.items():
                 trigger_scores[trigger] = trigger_scores.get(trigger, 0) + score
-            
-            current_progress += progress_per_chunk
-            if progress:
-                chunk_number = i + 1
-                progress(min(0.9, current_progress), 
-                        f"Processing chunk {chunk_number}/{len(chunks)}...")
 
         if progress:
-            progress(0.95, "Finalizing analysis...")
+            progress(0.95, "Finalizing results...")
 
         # Normalize scores by number of chunks and apply threshold
         chunk_count = len(chunks)
         final_triggers = [
             trigger for trigger, score in trigger_scores.items()
-            if score / chunk_count > 0.3
+            if score / chunk_count > 0.3  # Adjusted threshold for better balance
         ]
 
         return final_triggers if final_triggers else ["None"]
@@ -297,9 +287,6 @@ async def analyze_content(
     analyzer = ContentAnalyzer()
     
     try:
-        if progress:
-            progress(0.0, "Initializing analyzer...")
-        
         triggers = await analyzer.analyze_script(script, progress)
         
         if progress: