Update annotated_casl_app.py

annotated_casl_app.py

@@ -137,7 +137,10 @@ def combine_sections_smartly(sections_dict):
 
 
 def call_claude_api_quick_analysis(prompt):
-    """Call Claude API for quick focused analysis - single response only"""
+    """Call Claude API for quick focused analysis - single response only
+    Responses are cleaned to remove asterisks, hashtags, and convert simple tables to lists
+    to match formatting used in the main analysis pipeline.
+    """
     if not ANTHROPIC_API_KEY:
         return "❌ Claude API key not configured. Please set ANTHROPIC_API_KEY environment variable."
     
@@ -168,7 +171,16 @@ def call_claude_api_quick_analysis(prompt):
         
         if response.status_code == 200:
             response_json = response.json()
-            return response_json['content'][0]['text']
+            response_text = response_json['content'][0]['text']
+
+            # Clean formatting (remove asterisks, hashtags, convert simple tables) so
+            # Targeted Analysis and Quick Questions match the main analysis output
+            try:
+                cleaned = clean_output_formatting(response_text)
+            except Exception:
+                # If cleaning fails for any reason, fall back to raw response
+                cleaned = response_text
+            return cleaned
         else:
             logger.error(f"Claude API error: {response.status_code} - {response.text}")
             return f"❌ Claude API Error: {response.status_code}"
@@ -1545,140 +1557,88 @@ def analyze_with_backup(annotated_transcript, original_transcript, age, gender,
     - Count [REPETITION] markers: Categorize by type (word, phrase, sound)
     - Count [REVISION] markers: Analyze self-correction patterns
     - Count [PAUSE] markers: Assess hesitation frequency
-    - Calculate total disfluency rate
+    - Total disfluency assessment: Use verified total of {marker_analysis.get('category_totals', {}).get('fluency_issues', 0)} 
+      * Rate: {marker_analysis.get('category_totals', {}).get('fluency_issues', 0)/linguistic_metrics.get('total_words', 1)*100:.2f} per 100 words
+      * Provide objective rate calculation
 
     B. Word Retrieval Issues:
-    - Count [CIRCUMLOCUTION] markers: List each roundabout description
-    - Count [INCOMPLETE] markers: Analyze abandoned thought patterns
-    - Count [GENERIC] markers: Calculate specificity ratio
-    - Count [WORD_SEARCH] markers: Identify retrieval difficulty areas
-
-    C. Grammatical Errors:
-    - Count [GRAM_ERROR] markers by subcategory (verb tense, subject-verb agreement, etc.)
-    - Count [SYNTAX_ERROR] markers: Analyze word order problems
-    - Count [MORPH_ERROR] markers: Categorize morphological mistakes
-    - Count [RUN_ON] markers: Assess sentence boundary awareness
-
-    2. LANGUAGE SKILLS ASSESSMENT (with specific evidence):
+    - Circumlocutions: Count and analyze from transcript
+    - Incomplete thoughts: Identify abandoned utterances
+    - Generic language use: Count vague terms
+    - Word-finding efficiency: Assess retrieval success rate
 
-    A. Lexical/Semantic Skills:
-    - Use calculated Type-Token Ratio: {linguistic_metrics.get('type_token_ratio', 0)}
-    - Count [SIMPLE_VOCAB] vs [COMPLEX_VOCAB] markers
-    - Assess vocabulary sophistication ratio: {marker_analysis.get('category_totals', {}).get('vocab_sophistication_ratio', 0):.3f}
-    - Count [SEMANTIC_ERROR] markers and analyze patterns
+    C. Grammatical Errors (use verified counts):
+    - Grammar errors: Use verified count of {marker_counts.get('GRAM_ERROR', 0)}
+    - Syntax errors: Use verified count of {marker_counts.get('SYNTAX_ERROR', 0)}
+    - Morphological errors: Use verified count of {marker_counts.get('MORPH_ERROR', 0)}
+    - Calculate overall grammatical accuracy rate
 
-    B. Syntactic Skills:
-    - Count [SIMPLE_SENT], [COMPLEX_SENT], [COMPOUND_SENT] markers
-    - Calculate sentence complexity ratios
-    - Assess clause complexity and embedding
-
-    C. Supralinguistic Skills:
-    - Identify cause-effect relationships, inferences, non-literal language
-    - Assess problem-solving language and metalinguistic awareness
-
-    3. COMPLEX SENTENCE ANALYSIS (with exact counts):
-
-    A. Coordinating Conjunctions:
-    - Count and cite EVERY use of: and, but, or, so, yet, for, nor
-    - Analyze patterns and age-appropriateness
+    2. LANGUAGE SKILLS ASSESSMENT
 
-    B. Subordinating Conjunctions:
-    - Count and cite EVERY use of: because, although, while, since, if, when, where, that, which, who
-    - Analyze clause complexity and embedding depth
+    A. Vocabulary Analysis (use verified data):
+    - Simple vocabulary: Use verified count of {marker_counts.get('SIMPLE_VOCAB', 0)}
+    - Complex vocabulary: Use verified count of {marker_counts.get('COMPLEX_VOCAB', 0)}
+    - Sophistication ratio: Use verified ratio of {category_totals.get('vocab_sophistication_ratio', 0):.3f}
+    - Type-Token Ratio: Use verified TTR from basic metrics
+    - Provide examples of each vocabulary level from transcript
 
-    C. Sentence Structure Analysis:
-    - Use calculated MLU: {linguistic_metrics.get('mlu_words', 0)} words, {linguistic_metrics.get('mlu_morphemes', 0)} morphemes
-    - Calculate complexity ratios
+    B. Grammar and Morphology:
+    - Error pattern analysis using verified counts
+    - Pattern analysis only
+    - Morphological complexity evaluation
 
-    4. FIGURATIVE LANGUAGE ANALYSIS (with exact counts):
+    3. COMPLEX SENTENCE ANALYSIS (use verified counts)
 
-    A. Similes and Metaphors:
-    - Count [FIGURATIVE] markers for similes (using "like" or "as")
-    - Count [FIGURATIVE] markers for metaphors (direct comparisons)
+    A. Sentence Structure Distribution:
+    - Simple sentences: Use verified count of {marker_counts.get('SIMPLE_SENT', 0)}
+    - Complex sentences: Use verified count of {marker_counts.get('COMPLEX_SENT', 0)}
+    - Compound sentences: Use verified count of {marker_counts.get('COMPOUND_SENT', 0)}
+    - Calculate percentages of each type
 
-    B. Idioms and Non-literal Language:
-    - Count and analyze idiomatic expressions
-    - Assess comprehension and appropriate use
+    B. Syntactic Complexity:
+    - MLU analysis: Use verified MLU of {linguistic_metrics.get('mlu_words', 0):.2f} words
+    - Average sentence length: Use verified length of {linguistic_metrics.get('avg_sentence_length', 0):.2f} words
+    - Subordination and coordination patterns
 
-    5. PRAGMATIC LANGUAGE ASSESSMENT (with specific examples):
+    4. FIGURATIVE LANGUAGE ANALYSIS
+    - Figurative expressions: Use verified count of {marker_counts.get('FIGURATIVE', 0)}
+    - Metaphor and idiom identification from transcript
+    - Age-appropriate development assessment
+    - Abstract language abilities
 
-    A. Discourse Management:
-    - Count [TOPIC_SHIFT] markers: Assess transition appropriateness
-    - Count [TANGENT] markers: Analyze tangential speech patterns
-    - Count [COHERENCE_BREAK] markers: Assess logical flow
+    5. PRAGMATIC LANGUAGE ASSESSMENT
+    - Topic shifts: Use verified count of {marker_counts.get('TOPIC_SHIFT', 0)}
+    - Tangential speech: Use verified count of {marker_counts.get('TANGENT', 0)}
+    - Coherence breaks: Use verified count of {marker_counts.get('COHERENCE_BREAK', 0)}
+    - Referential clarity: Use verified count of {marker_counts.get('PRONOUN_REF', 0)}
+    - Overall conversational patterns observed
 
-    B. Referential Communication:
-    - Count [PRONOUN_REF] markers: Analyze referential clarity
-    - Assess communicative effectiveness
+    6. VOCABULARY AND SEMANTIC ANALYSIS
+    - Semantic errors: Use verified count of {marker_counts.get('SEMANTIC_ERROR', 0)}
+    - Lexical diversity: Use verified measures from stats summary
+    - Word association patterns from transcript analysis
+    - Semantic precision and appropriateness
 
-    6. VOCABULARY AND SEMANTIC ANALYSIS (with quantification):
+    7. MORPHOLOGICAL AND PHONOLOGICAL ANALYSIS
+    - Morphological complexity assessment
+    - Derivational and inflectional morphology patterns
+    - Error analysis using verified counts
+    - Pattern analysis only
 
-    A. Vocabulary Diversity:
-    - Total words: {linguistic_metrics.get('total_words', 0)}
+    8. QUANTITATIVE METRICS AND NLP FEATURES (use ALL verified data)
+    - Total words: {total_words}
+    - Total sentences: {linguistic_metrics.get('total_sentences', 0)}
     - Unique words: {linguistic_metrics.get('unique_words', 0)}
-    - Type-Token Ratio: {linguistic_metrics.get('type_token_ratio', 0)}
-    - Vocabulary sophistication: {linguistic_metrics.get('vocabulary_sophistication', 0)}
-
-    B. Semantic Relationships:
-    - Analyze word frequency patterns
-    - Assess semantic precision and relationships
-
-    7. MORPHOLOGICAL AND PHONOLOGICAL ANALYSIS (with counts):
+    - MLU words: {linguistic_metrics.get('mlu_words', 0):.2f}
+    - MLU morphemes: {linguistic_metrics.get('mlu_morphemes', 0):.2f}
+    - All error rates and ratios from verified counts
 
-    A. Morphological Markers:
-    - Count [MORPH_ERROR] markers and categorize
-    - Analyze morpheme use patterns
-    - Assess morphological complexity
 
-    B. Phonological Patterns:
-    - Identify speech sound patterns from transcript
-    - Assess syllable structure complexity
 
-    8. COGNITIVE-LINGUISTIC FACTORS (with evidence):
-
-    A. Working Memory:
-    - Assess sentence length complexity using average: {linguistic_metrics.get('avg_sentence_length', 0)} words
-    - Analyze information retention patterns
-
-    B. Processing Efficiency:
-    - Analyze linguistic complexity and word-finding patterns
-    - Assess cognitive demands of language structures
-
-    C. Executive Function:
-    - Count self-correction patterns ([REVISION] markers)
-    - Assess planning and organization in discourse
-
-    9. FLUENCY AND RHYTHM ANALYSIS (with quantification):
-
-    A. Disfluency Patterns:
-    - Total fluency issues: {marker_analysis.get('category_totals', {}).get('fluency_issues', 0)}
-    - Calculate disfluency rate per 100 words
-    - Analyze impact on communication
-
-    B. Language Flow:
-    - Assess sentence length variability: std = {linguistic_metrics.get('sentence_length_std', 0)}
-    - Analyze linguistic markers of hesitation
-
-    10. QUANTITATIVE METRICS:
-    - Total words: {linguistic_metrics.get('total_words', 0)}
-    - Total sentences: {linguistic_metrics.get('total_sentences', 0)}
-    - MLU (words): {linguistic_metrics.get('mlu_words', 0)}
-    - MLU (morphemes): {linguistic_metrics.get('mlu_morphemes', 0)}
-    - Type-Token Ratio: {linguistic_metrics.get('type_token_ratio', 0)}
-    - Grammar error rate: Calculate from marker counts
-    - Vocabulary sophistication ratio: {marker_analysis.get('category_totals', {}).get('vocab_sophistication_ratio', 0):.3f}
-
-    CRITICAL REQUIREMENTS:
-    - Use the provided calculated metrics in your analysis
-    - Provide EXACT counts for every marker type
-    - Calculate precise percentages and show your work
-    - Give specific examples from the transcript
-    - If annotation is incomplete, supplement with analysis of the original transcript
-    - Complete ALL 8 sections - use <CONTINUE> if needed
-    - Focus on objective data only - NO clinical interpretations
+    CRITICAL: Complete ALL 13 sections using verified data and specific transcript examples.
     """
     
-    return call_claude_api_with_continuation(analysis_prompt)
+    return call_claude_api_with_continuation(final_prompt)
 
 def full_analysis_pipeline(transcript_content, age, gender, slp_notes, progress_callback=None):
     """Complete pipeline: annotate then analyze with progressive updates"""
@@ -1690,6 +1650,7 @@ def full_analysis_pipeline(transcript_content, age, gender, slp_notes, progress_
     if progress_callback:
         progress_callback("🏷️ Step 1: Annotating transcript with linguistic markers...")
     
+    
     annotated_transcript = annotate_transcript(transcript_content, age, gender, slp_notes)
     
     if annotated_transcript.startswith("❌"):
@@ -1697,7 +1658,7 @@ def full_analysis_pipeline(transcript_content, age, gender, slp_notes, progress_
     
     # Return annotated transcript immediately
     if progress_callback:
-        progress_callback("✅ Step 1 Complete: Annotation finished! Starting analysis...")
+        progress_callback("Step 1 Complete: Annotation finished! Starting analysis...")
     
     # Check if annotation was incomplete
     if annotated_transcript.startswith("⚠️ ANNOTATION INCOMPLETE"):
@@ -1709,12 +1670,12 @@ def full_analysis_pipeline(transcript_content, age, gender, slp_notes, progress_
     # Step 2: Analyze annotated transcript with original as backup
     logger.info("Step 2: Analyzing annotated transcript...")
     if progress_callback:
-        progress_callback("📊 Step 2: Analyzing annotated transcript (this may take several minutes)...")
+        progress_callback("Step 2: Analyzing annotated transcript (this may take several minutes)...")
     
     analysis_result = analyze_with_backup(annotated_transcript, transcript_content, age, gender, slp_notes)
     
     if progress_callback:
-        progress_callback("✅ Analysis Complete!")
+        progress_callback("Analysis Complete!")
     
     return annotated_transcript, analysis_note + analysis_result
 
@@ -1726,7 +1687,7 @@ def progressive_analysis_pipeline(transcript_content, age, gender, slp_notes):
     
     # Step 1: Annotate transcript
     logger.info("Step 1: Annotating transcript with linguistic markers...")
-    yield "", "", "🏷️ Step 1: Annotating transcript with linguistic markers..."
+    yield "", "", "Step 1: Annotating transcript with linguistic markers..."
     
     annotated_transcript = annotate_transcript(transcript_content, age, gender, slp_notes)
     
@@ -1735,19 +1696,19 @@ def progressive_analysis_pipeline(transcript_content, age, gender, slp_notes):
         return
     
     # Return annotated transcript immediately after completion
-    yield annotated_transcript, "", "✅ Step 1 Complete! Starting analysis..."
+    yield annotated_transcript, "", "Step 1 Complete! Starting analysis..."
     
     # Check if annotation was incomplete
     if annotated_transcript.startswith("⚠️ ANNOTATION INCOMPLETE"):
         logger.warning("Annotation incomplete, proceeding with analysis")
-        analysis_note = "⚠️ Note: Annotation was incomplete. Analysis primarily based on original transcript.\n\n"
-        yield annotated_transcript, "", "⚠️ Annotation incomplete, continuing with analysis..."
+        analysis_note = "Note: Annotation was incomplete. Analysis primarily based on original transcript.\n\n"
+        yield annotated_transcript, "", "Annotation incomplete, continuing with analysis..."
     else:
         analysis_note = ""
     
     # Step 2: Analyze annotated transcript
     logger.info("Step 2: Analyzing annotated transcript...")
-    yield annotated_transcript, "", "📊 Step 2: Analyzing annotated transcript (this may take several minutes)..."
+    yield annotated_transcript, "", "Step 2: Analyzing annotated transcript (this may take several minutes)..."
     
     analysis_result = analyze_with_backup(annotated_transcript, transcript_content, age, gender, slp_notes)
     
@@ -1806,10 +1767,9 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
                     lines=3
                 )
                 
-                example_btn = gr.Button("📄 Load Example Transcript", variant="secondary", size="sm")
-                
-                # Single main analysis button
-                ultimate_analysis_btn = gr.Button("🚀 Run Complete Speech Analysis", variant="primary", size="lg")
+                with gr.Row():
+                    example_btn = gr.Button("Load Example Transcript", variant="secondary", size="sm")
+                    ultimate_analysis_btn = gr.Button("Run Complete Speech Analysis", variant="primary", size="lg")
             
             with gr.Column(scale=3):
                 status_display = gr.Markdown("Ready to analyze transcript")
@@ -1828,7 +1788,7 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
                     show_copy_button=True
                 )
     
-    with gr.Tab("🏷️ Annotation Only"):
+    with gr.Tab("Annotation Only"):
         gr.Markdown("### Step 1: Annotate transcript with linguistic markers")
         
         with gr.Row():
@@ -1852,8 +1812,9 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
                     lines=3
                 )
                 
-                example_btn_2 = gr.Button("📄 Load Example Transcript", variant="secondary", size="sm")
-                annotate_btn = gr.Button("🏷️ Annotate Transcript", variant="secondary")
+                with gr.Row():
+                    example_btn_2 = gr.Button("Load Example Transcript", variant="secondary", size="sm")
+                    annotate_btn = gr.Button("Annotate Transcript", variant="secondary")
             
             with gr.Column():
                 annotation_output = gr.Textbox(
@@ -1905,8 +1866,8 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
                     q5_btn = gr.Button("Word finding issues?", size="sm", variant="secondary")
                     q6_btn = gr.Button("Fluency problems?", size="sm", variant="secondary")
                 
-                example_btn_4 = gr.Button("📄 Load Example Transcript", variant="secondary", size="sm")
-                ask_question_btn = gr.Button("❓ Ask Question", variant="primary")
+                example_btn_4 = gr.Button("Load Example Transcript", variant="secondary", size="sm")
+                ask_question_btn = gr.Button("Ask Question", variant="primary")
             
             with gr.Column():
                 question_output = gr.Textbox(
@@ -1915,7 +1876,7 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
                     show_copy_button=True
                 )
     
-    with gr.Tab("🎯 Targeted Analysis"):
+    with gr.Tab("Targeted Analysis"):
         gr.Markdown("### Focus on specific areas of speech and language")
         
         with gr.Row():
@@ -1952,8 +1913,8 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
                     lines=2
                 )
                 
-                example_btn_5 = gr.Button("📄 Load Example Transcript", variant="secondary", size="sm")
-                targeted_analysis_btn = gr.Button("🎯 Run Targeted Analysis", variant="primary")
+                example_btn_5 = gr.Button("Load Example Transcript", variant="secondary", size="sm")
+                targeted_analysis_btn = gr.Button("Run Targeted Analysis", variant="primary")
             
             with gr.Column():
                 targeted_output = gr.Textbox(
@@ -1991,11 +1952,11 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
         annotated_transcript = annotate_transcript(transcript_content, age, gender, slp_notes)
         
         if annotated_transcript.startswith("❌"):
-            return annotated_transcript, "❌ Annotation failed"
+            return annotated_transcript, "Annotation failed"
         elif annotated_transcript.startswith("⚠️ ANNOTATION INCOMPLETE"):
-            return annotated_transcript, "⚠️ Annotation incomplete but proceeding"
+            return annotated_transcript, "Annotation incomplete but proceeding"
         else:
-            return annotated_transcript, "✅ Annotation complete! Click 'Run Analysis' to continue."
+            return annotated_transcript, "Annotation complete! Click 'Run Analysis' to continue."
     
     def run_analysis_step(annotated_transcript, original_transcript, age, gender, slp_notes):
         """Run the analysis step on the annotated transcript"""
@@ -2006,11 +1967,12 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
         
         # Check if annotation was incomplete
         if annotated_transcript.startswith("⚠️ ANNOTATION INCOMPLETE"):
-            analysis_note = "⚠️ Note: Annotation was incomplete. Analysis primarily based on original transcript.\n\n"
+            analysis_note = "Note: Annotation was incomplete. Analysis primarily based on original transcript.\n\n"
         else:
             analysis_note = ""
         
         analysis_result = analyze_with_backup(annotated_transcript, original_transcript, age, gender, slp_notes)
+        
         return analysis_note + analysis_result
     
     def run_manual_count_only(annotated_transcript):
@@ -2175,8 +2137,12 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
         - Repetitions: Use verified count of {marker_counts.get('REPETITION', 0)}
           * Categorize types (word, phrase, sound level)
           * Provide examples and count summary
-        - Total disfluency assessment: Use verified total of {category_totals['fluency_issues']} 
-          * Rate: {category_totals['fluency_issues']/total_words*100:.2f} per 100 words
+        - Revisions: Use verified count of {marker_counts.get('REVISION', 0)}
+          * Analyze self-correction patterns
+        - Pauses: Use verified count of {marker_counts.get('PAUSE', 0)}
+          * Assess hesitation frequency
+        - Total disfluency assessment: Use verified total of {category_totals.get('fluency_issues', 0)} 
+          * Rate: {category_totals.get('fluency_issues', 0)/linguistic_metrics.get('total_words', 1)*100:.2f} per 100 words
           * Provide objective rate calculation
 
         B. Word Retrieval Issues:
@@ -2196,7 +2162,7 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
         A. Vocabulary Analysis (use verified data):
         - Simple vocabulary: Use verified count of {marker_counts.get('SIMPLE_VOCAB', 0)}
         - Complex vocabulary: Use verified count of {marker_counts.get('COMPLEX_VOCAB', 0)}
-        - Sophistication ratio: Use verified ratio of {category_totals['vocab_sophistication_ratio']:.3f}
+        - Sophistication ratio: Use verified ratio of {category_totals.get('vocab_sophistication_ratio', 0):.3f}
         - Type-Token Ratio: Use verified TTR from basic metrics
         - Provide examples of each vocabulary level from transcript
 
@@ -2274,7 +2240,7 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
         # Step 2: Run analysis
         analysis_result = run_analysis_step(annotated_transcript, transcript_content, age, gender, slp_notes)
         
-        return annotated_transcript, analysis_result, "✅ Complete analysis finished!"
+        return annotated_transcript, analysis_result, "Complete analysis finished!"
     
     def run_complete_speech_analysis(transcript_content, age, gender, slp_notes):
         """Run the complete speech analysis pipeline with ultimate analysis"""
@@ -2290,7 +2256,7 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
         # Step 2: Run ultimate analysis
         ultimate_result = run_ultimate_analysis(annotated_transcript, transcript_content, age, gender, slp_notes)
         
-        return annotated_transcript, ultimate_result, "✅ Complete speech analysis finished!"
+        return annotated_transcript, ultimate_result, "Complete speech analysis finished!"
     
     # Single main event handler
     ultimate_analysis_btn.click(
@@ -2319,12 +2285,4 @@ with gr.Blocks(title="Speech Analysis", theme=gr.themes.Soft()) as demo:
         fn=analyze_targeted_area,
         inputs=[transcript_input_5, analysis_area, age_input_5, gender_input_5, slp_notes_input_5],
         outputs=[targeted_output]
-    )
-
-if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True,
-        show_error=True
     )