DEBUG_FIXES.md

ElectionWatch-ng Debug Fixes

This document details the specific issues encountered and their solutions.

🐛 Issues Identified

Issue 1: Text Analysis Not Generating Output

Problem:

• Text analysis only showed "Source" and "Content" but no actual AI analysis
• The LLM pipeline wasn't generating meaningful responses

Root Cause:

• Improper prompt formatting for Llama 3.1 model
• Missing chat template structure
• Insufficient error handling for edge cases

Solution:

# OLD (Broken):
prompt_template = f"""You are an AI assistant...
Content: {content_to_analyze}
Response:"""

# NEW (Fixed):
prompt_template = f"""<|start_header_id|>system<|end_header_id|>
You are an expert AI assistant specializing in election monitoring...
<|eot_id|>

<|start_header_id|>user<|end_header_id|>
Please analyze this content: {content_to_analyze}<|eot_id|>

<|start_header_id|>assistant<|end_header_id|>
"""

Issue 2: OCR Processing Error

Problem:

Error during OCR processing: You need to specify either text or text_target

Root Cause:

• TrOCR model expects specific parameter format for inference
• Missing generation parameters for proper OCR inference

Solution:

# FIXED: Direct processor call for TrOCR
pixel_values = ocr_processor(pil_image, return_tensors="pt").pixel_values

# Enhanced generation with proper parameters
generated_ids = ocr_model.generate(
    pixel_values,
    max_length=512,
    num_beams=4,
    early_stopping=True,
    pad_token_id=ocr_processor.tokenizer.pad_token_id,
    eos_token_id=ocr_processor.tokenizer.eos_token_id
)

Issue 3: Text Formatting

Problem:

• Text analysis output was compacted in paragraphs
• Difficult to read and extract key information

Root Cause:

• Prompt didn't specify clear formatting requirements
• Model wasn't instructed to use bullet points and line breaks

Solution: Enhanced prompt template with explicit formatting instructions:

prompt_template = f"""
Provide a comprehensive analysis in the following format. Use bullet points and put each main point on a separate line:

1. **Misinformation Risk Level**: (Low/Medium/High)
   - Provide your assessment and reasoning

2. **Identified Narratives**: List specific misinformation themes from DISARM
   - List each narrative on a separate line
   - Reference specific DISARM techniques if applicable

Format your response with clear line breaks between sections and bullet points for readability.
"""

🔧 Applied Fixes

1. Enhanced OCR Processing

def analyze_content(input_type, text_input, image_input):
    if input_type == "Image Input":
        try:
            # Convert to PIL Image if needed
            if hasattr(image_input, 'convert'):
                pil_image = image_input.convert('RGB')
            else:
                pil_image = Image.fromarray(image_input).convert('RGB')
            
            # FIXED: Use correct processor format
            inputs = ocr_processor(images=pil_image, return_tensors="pt")
            pixel_values = inputs.pixel_values
            
            # Generate with proper parameters
            with torch.no_grad():
                generated_ids = ocr_model.generate(pixel_values, max_length=512)
            
            extracted_text = ocr_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
            # ... rest of processing

2. Improved LLM Text Generation

# FIXED: Proper Llama 3.1 chat format
prompt_template = f"""<|start_header_id|>system<|end_header_id|>
You are an expert AI assistant specializing in election monitoring and misinformation analysis.

Provide a comprehensive analysis in the following format:
1. **Misinformation Risk Level**: (Low/Medium/High)
2. **Identified Narratives**: List specific misinformation themes
3. **Potential Propagators**: Who might be spreading this
4. **Target Audience**: Who is this content targeting
5. **Verification Priority**: Which claims need immediate fact-checking
6. **Analysis Summary**: Overall assessment<|eot_id|>

<|start_header_id|>user<|end_header_id|>
Please analyze this content for election-related misinformation:

{content_to_analyze}<|eot_id|>

<|start_header_id|>assistant<|end_header_id|>
"""

# Enhanced generation parameters
outputs = llm_pipeline(
    prompt_template,
    max_new_tokens=800,  # Increased for detailed analysis
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.1,
    do_sample=True,
    return_full_text=False,
    pad_token_id=llm_tokenizer.eos_token_id if llm_tokenizer else None,
    eos_token_id=llm_tokenizer.eos_token_id if llm_tokenizer else None
)

3. Enhanced Error Handling

# Better error messages and debugging
try:
    # ... processing code ...
except Exception as e:
    print(f"Error Details: {e}")
    import traceback
    traceback.print_exc()
    return f"""## Analysis Results

**Source**: {analysis_source}
**Content**: {content_to_analyze}

---

**Error**: Analysis failed due to: {str(e)}

This might be due to:
- Model inference issues
- Memory constraints  
- Content length too long
- Model loading problems

Please try:
1. Using shorter content
2. Restarting the interface
3. Using text input instead of image input
"""

🧪 Testing the Fixes

Run the Debug Script

cd hf-space
python debug_test.py

This will test:

• ✅ Environment setup
• ✅ OCR model loading with correct format
• ✅ LLM model loading and generation
• ✅ Proper prompt formatting

Expected Test Results

=== OCR Model Test ===
✓ OCR processor loaded successfully
✓ OCR model loaded successfully
✓ OCR preprocessing successful, tensor shape: torch.Size([1, 3, 384, 384])
✓ OCR generation successful

=== LLM Model Test ===
✓ LLM tokenizer loaded successfully
✓ LLM model loaded successfully
✓ LLM pipeline created successfully
✓ LLM generation successful!
✓ Generated response has good length

🎉 All tests passed! The updated app should work properly.

🚀 Deployment Steps

1. Upload Fixed Files:

   # Upload the updated app.py to your HuggingFace Space
   git add app.py debug_test.py
   git commit -m "Fix OCR processing and LLM text generation issues"
   git push origin main
   

2. Verify Automatic Restart:

   • HuggingFace Spaces will automatically restart
   • Check the build logs for any errors
   • Wait for "Running" status

3. Test Both Input Types:

   • Text Input: Should now generate detailed analysis
   • Image Input: Should extract text and analyze without errors

📊 Expected Improvements

Before Fix:

• Text Analysis: Only showed source and content, compacted output
• Image Analysis: OCR processing error ("You need to specify either text or text_target")
• User Experience: Confusing error messages, hard to read output

After Fix:

• Text Analysis: ✅ Generates comprehensive misinformation analysis with bullet points
• Image Analysis: ✅ Extracts text and provides full analysis with fallback methods
• User Experience: ✅ Clear error messages, readable formatting, and debugging info

🔍 Monitoring

Success Indicators:

1. Text input generates analysis with properly formatted sections:

   • Misinformation Risk Level: (Low/Medium/High) with bullet points
   • Identified Narratives: DISARM-referenced themes on separate lines
   • Potential Propagators: Listed with motivations
   • Target Audience: Demographic groups and targeting strategy
   • Verification Priority: Prioritized claims with DISARM references
   • Analysis Summary: Concluding thoughts and recommended actions

2. Image input works end-to-end:

   • OCR extraction succeeds with enhanced parameters
   • Fallback OCR method available if primary fails
   • Extracted text is analyzed with same formatting as text input
   • Full analysis is displayed with proper bullet points

3. Error handling is informative:

   • Clear error messages with specific causes
   • Alternative processing attempts
   • Debugging information and troubleshooting steps
   • Actionable recommendations for users

Common Issues to Watch:

• Memory constraints with large images
• Model loading timeouts
• Long content causing generation issues

📝 Future Improvements

1. Performance Optimization:

   • Implement model caching
   • Add request queuing for high load
   • Optimize memory usage

2. Enhanced Features:

   • Support for multiple image formats
   • Batch processing capabilities
   • Custom analysis templates

3. Better UX:

   • Progress indicators
   • Real-time status updates
   • Analysis confidence scores