Update email_gen.py

email_gen.py

@@ -1,561 +1,585 @@
-import os
-import json
-from llama_cpp import Llama
-import re
-from huggingface_hub import hf_hub_download
-import random
-
-class EmailGenerator:
-    def __init__(self, custom_model_path=None):
-        self.model = None
-        self.model_path = custom_model_path or self._download_model()
-        self._load_model()
-        self.prompt_templates = self._load_prompt_templates()
-    
-    def _download_model(self):
-        """Download Vicuna-7B GGUF model from Hugging Face"""
-        try:
-            model_name = "TheBloke/vicuna-7B-v1.5-GGUF"
-            filename = "vicuna-7b-v1.5.Q4_K_M.gguf"
-            
-            print("Downloading Vicuna-7B model... This may take a while.")
-            model_path = hf_hub_download(
-                repo_id=model_name,
-                filename=filename,
-                cache_dir="./models"
-            )
-            print(f"Model downloaded to: {model_path}")
-            return model_path
-        except Exception as e:
-            print(f"Error downloading model: {e}")
-            return None
-    
-    def _load_model(self):
-        """Load the GGUF model using llama-cpp-python"""
-        try:
-            if self.model_path and os.path.exists(self.model_path):
-                print(f"🤖 Loading language model from: {self.model_path}")
-                self.model = Llama(
-                    model_path=self.model_path,
-                    n_ctx=2048,  # Context length
-                    n_threads=2,  # Reduced for stability
-                    n_batch=512,  # Batch size
-                    verbose=False,
-                    use_mmap=True,  # Memory mapping for efficiency
-                    use_mlock=False  # Don't lock memory
-                )
-                print("✅ Model loaded successfully!")
-                
-                # Test the model with a simple prompt
-                test_response = self.model("Test", max_tokens=5, temperature=0.1)
-                if test_response and 'choices' in test_response:
-                    print("✅ Model test successful")
-                else:
-                    print("⚠️ Model test failed, will use fallback")
-                    self.model = None
-                    
-            else:
-                print("❌ No valid model path found. Using advanced fallback generation.")
-                self.model = None
-        except Exception as e:
-            print(f"❌ Error loading model: {e}")
-            print("🔄 Will use advanced fallback generation system")
-            self.model = None
-    
-    def _generate_with_model(self, prompt, max_tokens=250, temperature=0.7):
-        """Generate text using the loaded model with retry logic"""
-        try:
-            if self.model:
-                # First attempt
-                response = self.model(
-                    prompt,
-                    max_tokens=max_tokens,
-                    temperature=temperature,
-                    top_p=0.9,
-                    stop=["</s>", "\n\n\n", "EXAMPLE", "Now write"],
-                    echo=False
-                )
-                result = response['choices'][0]['text'].strip()
-                
-                # Check if result is valid
-                if self._is_valid_output(result):
-                    return result
-                
-                # Retry with different temperature if first attempt failed
-                print("First attempt failed, retrying with adjusted parameters...")
-                response = self.model(
-                    prompt,
-                    max_tokens=max_tokens,
-                    temperature=min(temperature + 0.2, 1.0),
-                    top_p=0.8,
-                    stop=["</s>", "\n\n\n", "EXAMPLE", "Now write"],
-                    echo=False
-                )
-                return response['choices'][0]['text'].strip()
-            else:
-                return self._fallback_generation(prompt)
-        except Exception as e:
-            print(f"Error generating with model: {e}")
-            return self._fallback_generation(prompt)
-    
-    def _is_valid_output(self, output):
-        """Check if the generated output is valid"""
-        if not output or len(output) < 20:
-            return False
-        
-        # Check for incomplete JSON
-        if '{' in output and '}' not in output:
-            return False
-        
-        # Check for common failure patterns
-        failure_patterns = [
-            'I cannot', 'I apologize', 'I\'m sorry',
-            '[Your Name]', '[Company]', '[Product]',
-            'EXAMPLE', 'Now write'
-        ]
-        
-        return not any(pattern in output for pattern in failure_patterns)
-    
-    def _parse_json_response(self, response):
-        """Parse JSON response from the model"""
-        try:
-            # Clean up the response
-            response = response.strip()
-            
-            # Extract JSON if it's embedded in text
-            json_match = re.search(r'\{[^}]*"subject"[^}]*\}', response, re.DOTALL)
-            if json_match:
-                response = json_match.group(0)
-            
-            # Parse JSON
-            data = json.loads(response)
-            
-            subject = data.get('subject', '').strip()
-            body = data.get('body', '').strip()
-            
-            # Clean up quotes and formatting
-            subject = subject.strip('"\'')
-            body = body.strip('"\'')
-            
-            return subject, body
-            
-        except (json.JSONDecodeError, KeyError) as e:
-            print(f"JSON parsing error: {e}")
-            return self._extract_fallback_content(response)
-    
-    def _extract_fallback_content(self, response):
-        """Extract subject and body from non-JSON response"""
-        lines = response.split('\n')
-        subject = ""
-        body = ""
-        
-        # Look for subject line
-        for line in lines:
-            if any(word in line.lower() for word in ['subject:', 'subj:', 'sub:']):
-                subject = re.sub(r'^[^:]*:', '', line).strip()
-                break
-        
-        # Look for body
-        body_started = False
-        body_lines = []
-        for line in lines:
-            if body_started:
-                if line.strip():
-                    body_lines.append(line.strip())
-            elif any(word in line.lower() for word in ['body:', 'email:', 'hi ', 'dear ', 'hello ']):
-                body_started = True
-                clean_line = re.sub(r'^[^:]*:', '', line).strip()
-                if clean_line and not clean_line.lower().startswith(('body', 'email')):
-                    body_lines.append(clean_line)
-        
-        body = '\n'.join(body_lines) if body_lines else response
-        
-        # Fallback if parsing failed
-        if not subject:
-            subject = f"Partnership opportunity"
-        if not body or len(body) < 20:
-            body = "Hi,\n\nI'd love to explore how we can help your business grow.\n\nInterested in a quick call?\n\nBest regards"
-        
-        return subject, body
-    
-    def _advanced_fallback_generation(self, name, company, company_info, tone="Professional"):
-        """Advanced fallback with company-specific personalization"""
-        
-        # Extract industry and key details from company info
-        industry_hints = self._extract_industry_details(company_info)
-        
-        # Create tone-specific templates
-        if tone.lower() == "friendly":
-            templates = [
-                {
-                    "subject": f"Love what {company} is doing{industry_hints['subject_suffix']}",
-                    "body": f"Hi {name},\n\nJust came across {company}{industry_hints['context']} - really impressive work!\n\nWe've helped similar {industry_hints['industry']} companies {industry_hints['benefit']}. Mind if I share a quick example?\n\n15-minute call work for you?\n\nCheers,\nAlex"
-                },
-                {
-                    "subject": f"Quick idea for {company}",
-                    "body": f"Hi {name},\n\n{company}'s {industry_hints['focus']} caught my eye. We just helped a similar company {industry_hints['specific_result']}.\n\nWorth exploring for {company}?\n\nBest,\nSam"
-                }
-            ]
-        elif tone.lower() == "direct":
-            templates = [
-                {
-                    "subject": f"{company} + {industry_hints['solution']}?",
-                    "body": f"Hi {name},\n\n{industry_hints['direct_opener']} for {company}.\n\nResult: {industry_hints['specific_result']}.\n\nInterested? 10-minute call?\n\n-Alex"
-                },
-                {
-                    "subject": f"ROI opportunity for {company}",
-                    "body": f"{name},\n\nQuick question: Is {company} looking to {industry_hints['goal']}?\n\nWe reduced costs by 35% for a similar {industry_hints['industry']} company.\n\nWorth a conversation?\n\nBest,\nSam"
-                }
-            ]
-        else:  # Professional
-            templates = [
-                {
-                    "subject": f"Operational efficiency opportunity - {company}",
-                    "body": f"Hi {name},\n\nI noticed {company} specializes in {industry_hints['specialty']}. We recently helped a similar organization {industry_hints['professional_result']}.\n\nWould you be open to a brief conversation about how this might apply to {company}?\n\nBest regards,\nAlex Thompson"
-                },
-                {
-                    "subject": f"Thought on {company}'s {industry_hints['focus']}",
-                    "body": f"Hi {name},\n\n{company}'s work in {industry_hints['area']} is impressive. We've developed solutions that help {industry_hints['industry']} companies {industry_hints['benefit']}.\n\nWould you be interested in a 15-minute discussion about potential applications for {company}?\n\nBest regards,\nSarah Chen"
-                }
-            ]
-        
-        template = random.choice(templates)
-        return template["subject"], template["body"]
-    
-    def _extract_industry_details(self, company_info):
-        """Extract industry-specific details for personalization"""
-        info_lower = company_info.lower() if company_info else ""
-        
-        if any(word in info_lower for word in ['tech', 'software', 'saas', 'ai', 'digital']):
-            return {
-                'industry': 'tech',
-                'specialty': 'technology solutions',
-                'focus': 'innovation',
-                'area': 'technology',
-                'benefit': 'scale their platforms and reduce technical debt',
-                'goal': 'optimize your development pipeline',
-                'solution': 'DevOps automation',
-                'context': ' and their tech stack',
-                'subject_suffix': ' with tech',
-                'direct_opener': 'We implemented automated testing',
-                'specific_result': 'reduced deployment time by 60%',
-                'professional_result': 'achieve 40% faster time-to-market for new features'
-            }
-        elif any(word in info_lower for word in ['manufactur', 'industrial', 'equipment', 'materials']):
-            return {
-                'industry': 'manufacturing',
-                'specialty': 'industrial operations',
-                'focus': 'production efficiency',
-                'area': 'manufacturing',
-                'benefit': 'optimize their production lines and reduce waste',
-                'goal': 'increase production efficiency',
-                'solution': 'process optimization',
-                'context': ' and their manufacturing capabilities',
-                'subject_suffix': ' in manufacturing',
-                'direct_opener': 'We streamlined production workflows',
-                'specific_result': 'increased throughput by 45%',
-                'professional_result': 'achieve 30% improvement in production efficiency'
-            }
-        elif any(word in info_lower for word in ['health', 'medical', 'pharma', 'clinical']):
-            return {
-                'industry': 'healthcare',
-                'specialty': 'healthcare solutions',
-                'focus': 'patient outcomes',
-                'area': 'healthcare',
-                'benefit': 'improve patient outcomes while reducing costs',
-                'goal': 'enhance patient care efficiency',
-                'solution': 'workflow optimization',
-                'context': ' and their patient care approach',
-                'subject_suffix': ' in healthcare',
-                'direct_opener': 'We optimized patient flow systems',
-                'specific_result': 'reduced wait times by 50%',
-                'professional_result': 'achieve 25% improvement in patient satisfaction scores'
-            }
-        else:
-            return {
-                'industry': 'business',
-                'specialty': 'business operations',
-                'focus': 'growth',
-                'area': 'operations',
-                'benefit': 'streamline operations and drive growth',
-                'goal': 'scale your operations',
-                'solution': 'process optimization',
-                'context': ' and their business model',
-                'subject_suffix': '',
-                'direct_opener': 'We automated key business processes',
-                'specific_result': 'increased efficiency by 40%',
-                'professional_result': 'achieve 35% operational cost reduction'
-            }
-    
-    def _load_prompt_templates(self):
-        """Load sophisticated prompt templates for different use cases"""
-        return {
-            "few_shot_template": '''You are an elite B2B sales copywriter. Write ONE personalized cold email that sounds natural and converts.
-
-<examples>
-EXAMPLE 1:
-SUBJECT: Quick question about Acme's EU expansion
-BODY: Hi Sarah,
-
-Saw Acme just launched in Berlin – congrats! We helped Contoso reduce their GDPR compliance prep by 68% with a simple automation.
-
-Worth a 10-minute chat about how this could apply to your EU rollout?
-
-Best,
-Alex
-
-EXAMPLE 2:
-SUBJECT: Thought on TechCorp's materials testing
-BODY: Hi John,
-
-Noticed TechCorp specializes in X-ray spectroscopy equipment. We just helped a similar lab increase throughput 40% with workflow optimization.
-
-Mind if I share what worked for them? 15-minute call?
-
-Best,
-Sam
-
-EXAMPLE 3:
-SUBJECT: Manufacturing efficiency idea for IndustrialCorp
-BODY: Hi Mike,
-
-IndustrialCorp's production line setup caught my attention. We automated similar processes for MetalWorks, reducing their cycle time by 35%.
-
-Open to a brief conversation about applications for your facility?
-
-Best regards,
-Jennifer
-</examples>
-
-Now write an email for:
-Name: {name}
-Company: {company}
-Company Info: {company_context}
-Tone: {tone}
-
-Requirements:
-- Use the company info naturally in the first 2 lines
-- Maximum 70 words in body (excluding signature)
-- Clear yes/no question at the end
-- No placeholders like [Your Name] or [Company]
-- Professional but conversational
-- Include specific benefit or result if possible
-
-Return ONLY this JSON format:
-{{"subject": "...", "body": "..."}}''',
-
-            "industry_specific": {
-                "technology": '''Write a cold email for a tech company. Focus on efficiency, scalability, and competitive advantage.''',
-                "healthcare": '''Write a cold email for a healthcare company. Focus on patient outcomes, compliance, and cost reduction.''',
-                "manufacturing": '''Write a cold email for a manufacturing company. Focus on production efficiency, quality, and cost savings.''',
-                "services": '''Write a cold email for a service company. Focus on client satisfaction, process improvement, and growth.''',
-                "default": '''Write a cold email that focuses on business growth and operational efficiency.'''
-            }
-        }
-    
-    def _extract_industry(self, company_info):
-        """Extract industry type from company information"""
-        company_lower = company_info.lower()
-        
-        if any(word in company_lower for word in ['tech', 'software', 'saas', 'ai', 'digital', 'app', 'platform']):
-            return 'technology'
-        elif any(word in company_lower for word in ['health', 'medical', 'pharma', 'hospital', 'clinic']):
-            return 'healthcare'
-        elif any(word in company_lower for word in ['manufactur', 'factory', 'production', 'industrial', 'equipment']):
-            return 'manufacturing'
-        elif any(word in company_lower for word in ['service', 'consulting', 'agency', 'firm']):
-            return 'services'
-        else:
-            return 'default'
-    
-    def _create_company_context(self, company, company_info):
-        """Create focused company context for the prompt"""
-        # Extract key information and clean it up
-        context_parts = []
-        
-        if company_info and len(company_info) > 10:
-            # Extract meaningful phrases
-            sentences = re.split(r'[.!?]+', company_info)
-            for sentence in sentences[:3]:  # First 3 sentences
-                sentence = sentence.strip()
-                if len(sentence) > 20 and not sentence.startswith('Title:'):
-                    # Remove common fluff words
-                    sentence = re.sub(r'Description:\s*', '', sentence)
-                    sentence = re.sub(r'Company Website:\s*', '', sentence)
-                    sentence = re.sub(r'LinkedIn:\s*', '', sentence)
-                    if sentence:
-                        context_parts.append(sentence)
-        
-        if not context_parts:
-            context_parts.append(f"{company} is a company in their industry")
-        
-        return ' | '.join(context_parts[:2])  # Max 2 key points
-    
-    def generate_email(self, name, company, company_info, tone="Professional", temperature=0.7):
-        """Generate both subject and email body using advanced prompting"""
-        # Clean up and prepare context
-        company_context = self._create_company_context(company, company_info)
-        
-        # Calibrate temperature for production readiness
-        if temperature > 1.0:
-            temperature = 0.8  # Cap at 0.8 for production readiness
-        
-        # Try AI generation first
-        if self.model:
-            try:
-                # Build the prompt using few-shot template
-                prompt = self.prompt_templates["few_shot_template"].format(
-                    name=name,
-                    company=company,
-                    company_context=company_context,
-                    tone=tone.lower()
-                )
-                
-                # Generate with model
-                response = self._generate_with_model(prompt, max_tokens=200, temperature=temperature)
-                
-                # Parse the response
-                subject, body = self._parse_json_response(response)
-                
-                # Polish the content
-                subject, body = self._polish_email_content(subject, body)
-                
-                # Validate quality
-                quality_score, issues = self._validate_email_quality(subject, body, name, company)
-                
-                # If quality is good enough, return it
-                if quality_score >= 70 and 'placeholders' not in issues:
-                    print(f"✅ AI generated email (Quality: {quality_score}%)")
-                    return subject, body
-                else:
-                    print(f"⚠️ AI output quality too low ({quality_score}%), using advanced fallback")
-                    
-            except Exception as e:
-                print(f"❌ AI generation failed: {e}, using advanced fallback")
-        
-        # Use advanced fallback system
-        print("🔄 Using advanced fallback generation")
-        subject, body = self._advanced_fallback_generation(name, company, company_info, tone)
-        
-        # Always polish fallback content
-        subject, body = self._polish_email_content(subject, body)
-        
-        return subject, body
-    
-    def _clean_subject(self, subject, company):
-        """Clean and validate subject line"""
-        if not subject or len(subject) < 5:
-            return f"Quick question about {company}"
-        
-        # Remove common prefixes
-        subject = re.sub(r'^(Subject|SUBJECT):\s*', '', subject, flags=re.IGNORECASE)
-        subject = subject.strip('"\'')
-        
-        # Ensure reasonable length
-        if len(subject) > 60:
-            subject = subject[:57] + "..."
-        
-        return subject
-    
-    def _clean_body(self, body, name):
-        """Clean and validate email body"""
-        if not body or len(body) < 20:
-            return f"Hi {name},\n\nI'd love to discuss how we can help your business grow.\n\nInterested in a quick call?\n\nBest regards"
-        
-        # Remove common prefixes
-        body = re.sub(r'^(Body|BODY|Email|EMAIL):\s*', '', body, flags=re.IGNORECASE)
-        
-        # Ensure proper greeting
-        if not body.lower().startswith(('hi ', 'hello ', 'dear ')):
-            body = f"Hi {name},\n\n{body}"
-        
-        # Ensure proper closing
-        closing_patterns = ['best regards', 'best,', 'sincerely', 'regards,', 'cheers,']
-        has_closing = any(pattern in body.lower() for pattern in closing_patterns)
-        
-        if not has_closing:
-            if not body.endswith('\n'):
-                body += '\n'
-            body += '\nBest regards'
-        
-        return body
-    
-    def _polish_email_content(self, subject, body):
-        """Polish email content for grammar and professionalism"""
-        
-        # Fix common grammar issues
-        body = re.sub(r'\s+', ' ', body)  # Multiple spaces
-        body = re.sub(r'([.!?])\s*([a-z])', r'\1 \2', body)  # Space after punctuation
-        body = re.sub(r'(\w)\s*\n\s*(\w)', r'\1\n\n\2', body)  # Proper paragraph spacing
-        
-        # Ensure professional closing
-        if not re.search(r'(Best regards|Best|Sincerely|Cheers),?\s*\n?[A-Z][a-z]+', body):
-            if body.strip().endswith(','):
-                body = body.strip() + '\n\nBest regards,\nAlex'
-            else:
-                body = body.strip() + '\n\nBest regards,\nAlex'
-        
-        # Fix subject line
-        subject = subject.strip()
-        if len(subject) > 65:
-            subject = subject[:62] + "..."
-        
-        # Capitalize first letter of subject if not already
-        if subject and subject[0].islower():
-            subject = subject[0].upper() + subject[1:]
-        
-        return subject, body
-    
-    def _validate_email_quality(self, subject, body, name, company):
-        """Validate email quality and return quality score"""
-        issues = []
-        
-        # Check subject length
-        if len(subject) < 10 or len(subject) > 65:
-            issues.append("subject_length")
-        
-        # Check body length
-        words = len(body.split())
-        if words < 20 or words > 150:
-            issues.append("body_length")
-        
-        # Check for placeholders
-        if '[Your Name]' in body or '[Company]' in body or '{{' in body:
-            issues.append("placeholders")
-        
-        # Check personalization
-        if name not in body or company not in body:
-            issues.append("personalization")
-        
-        # Check for call-to-action
-        cta_phrases = ['call', 'conversation', 'chat', 'discuss', 'talk', 'meeting', 'connect']
-        if not any(phrase in body.lower() for phrase in cta_phrases):
-            issues.append("no_cta")
-        
-        quality_score = max(0, 100 - (len(issues) * 15))
-        return quality_score, issues
-    
-    def generate_multiple_variations(self, name, company, company_info, num_variations=3, tone="Professional"):
-        """Generate multiple email variations with different approaches"""
-        variations = []
-        tones = ["Professional", "Friendly", "Direct"]
-        temperatures = [0.6, 0.7, 0.8]
-        
-        for i in range(num_variations):
-            current_tone = tones[i % len(tones)]
-            current_temp = temperatures[i % len(temperatures)]
-            
-            subject, email_body = self.generate_email(
-                name, company, company_info, 
-                tone=current_tone, temperature=current_temp
-            )
-            
-            variations.append({
-                'variation': i + 1,
-                'tone': current_tone,
-                'temperature': current_temp,
-                'subject': subject,
-                'email_body': email_body
-            })
-        
-        return variations
+import os
+import json
+from llama_cpp import Llama
+import re
+from huggingface_hub import hf_hub_download
+import random
+
+class EmailGenerator:
+    def __init__(self, custom_model_path=None):
+        self.model = None
+        self.model_path = custom_model_path or self._download_model()
+        self._load_model()
+        self.prompt_templates = self._load_prompt_templates()
+    
+    def _download_model(self):
+        """Download Vicuna-7B GGUF model from Hugging Face"""
+        try:
+            model_name = "TheBloke/vicuna-7B-v1.5-GGUF"
+            filename = "vicuna-7b-v1.5.Q4_K_M.gguf"
+            
+            print("Downloading Vicuna-7B model... This may take a while.")
+            model_path = hf_hub_download(
+                repo_id=model_name,
+                filename=filename,
+                cache_dir="./models"
+            )
+            print(f"Model downloaded to: {model_path}")
+            return model_path
+        except Exception as e:
+            print(f"Error downloading model: {e}")
+            return None
+    
+    def _load_model(self):
+        """Load the GGUF model using llama-cpp-python"""
+        try:
+            if self.model_path and os.path.exists(self.model_path):
+                print(f"🤖 Loading language model from: {self.model_path}")
+                self.model = Llama(
+                    model_path=self.model_path,
+                    n_ctx=2048,  # Context length
+                    n_threads=2,  # Reduced for stability
+                    n_batch=512,  # Batch size
+                    verbose=False,
+                    use_mmap=True,  # Memory mapping for efficiency
+                    use_mlock=False  # Don't lock memory
+                )
+                print("✅ Model loaded successfully!")
+                
+                # Test the model with a simple prompt
+                test_response = self.model("Test", max_tokens=5, temperature=0.1)
+                if test_response and 'choices' in test_response:
+                    print("✅ Model test successful")
+                else:
+                    print("⚠️ Model test failed, will use fallback")
+                    self.model = None
+                    
+            else:
+                print("❌ No valid model path found. Using advanced fallback generation.")
+                self.model = None
+        except Exception as e:
+            print(f"❌ Error loading model: {e}")
+            print("🔄 Will use advanced fallback generation system")
+            self.model = None
+    
+    def _generate_with_model(self, prompt, max_tokens=250, temperature=0.7):
+        """Generate text using the loaded model with retry logic"""
+        try:
+            if self.model:
+                # First attempt
+                response = self.model(
+                    prompt,
+                    max_tokens=max_tokens,
+                    temperature=temperature,
+                    top_p=0.9,
+                    stop=["</s>", "\n\n\n", "EXAMPLE", "Now write"],
+                    echo=False
+                )
+                result = response['choices'][0]['text'].strip()
+                
+                # Check if result is valid
+                if self._is_valid_output(result):
+                    return result
+                
+                # Retry with different temperature if first attempt failed
+                print("First attempt failed, retrying with adjusted parameters...")
+                response = self.model(
+                    prompt,
+                    max_tokens=max_tokens,
+                    temperature=min(temperature + 0.2, 1.0),
+                    top_p=0.8,
+                    stop=["</s>", "\n\n\n", "EXAMPLE", "Now write"],
+                    echo=False
+                )
+                return response['choices'][0]['text'].strip()
+            else:
+                return self._fallback_generation(prompt)
+        except Exception as e:
+            print(f"Error generating with model: {e}")
+            return self._fallback_generation(prompt)
+    
+    def _is_valid_output(self, output):
+        """Check if the generated output is valid"""
+        if not output or len(output) < 20:
+            return False
+        
+        # Check for incomplete JSON
+        if '{' in output and '}' not in output:
+            return False
+        
+        # Check for common failure patterns
+        failure_patterns = [
+            'I cannot', 'I apologize', 'I\'m sorry',
+            '[Your Name]', '[Company]', '[Product]',
+            'EXAMPLE', 'Now write'
+        ]
+        
+        return not any(pattern in output for pattern in failure_patterns)
+    
+    def _parse_json_response(self, response):
+        """Parse JSON response from the model"""
+        try:
+            # Clean up the response
+            response = response.strip()
+            
+            # Extract JSON if it's embedded in text
+            json_match = re.search(r'\{[^}]*"subject"[^}]*\}', response, re.DOTALL)
+            if json_match:
+                response = json_match.group(0)
+            
+            # Parse JSON
+            data = json.loads(response)
+            
+            subject = data.get('subject', '').strip()
+            body = data.get('body', '').strip()
+            
+            # Clean up quotes and formatting
+            subject = subject.strip('"\'')
+            body = body.strip('"\'')
+            
+            return subject, body
+            
+        except (json.JSONDecodeError, KeyError) as e:
+            print(f"JSON parsing error: {e}")
+            return self._extract_fallback_content(response)
+    
+    def _extract_fallback_content(self, response):
+        """Extract subject and body from non-JSON response"""
+        lines = response.split('\n')
+        subject = ""
+        body = ""
+        
+        # Look for subject line
+        for line in lines:
+            if any(word in line.lower() for word in ['subject:', 'subj:', 'sub:']):
+                subject = re.sub(r'^[^:]*:', '', line).strip()
+                break
+        
+        # Look for body
+        body_started = False
+        body_lines = []
+        for line in lines:
+            if body_started:
+                if line.strip():
+                    body_lines.append(line.strip())
+            elif any(word in line.lower() for word in ['body:', 'email:', 'hi ', 'dear ', 'hello ']):
+                body_started = True
+                clean_line = re.sub(r'^[^:]*:', '', line).strip()
+                if clean_line and not clean_line.lower().startswith(('body', 'email')):
+                    body_lines.append(clean_line)
+        
+        body = '\n'.join(body_lines) if body_lines else response
+        
+        # Fallback if parsing failed
+        if not subject:
+            subject = f"Partnership opportunity"
+        if not body or len(body) < 20:
+            body = "Hi,\n\nI'd love to explore how we can help your business grow.\n\nInterested in a quick call?\n\nBest regards"
+        
+        return subject, body
+    
+    def _advanced_fallback_generation(self, name, company, company_info, tone="Professional"):
+        """Advanced fallback with company-specific personalization"""
+        
+        # Extract industry and key details from company info
+        industry_hints = self._extract_industry_details(company_info)
+        
+        # Create tone-specific templates
+        if tone.lower() == "friendly":
+            templates = [
+                {
+                    "subject": f"Love what {company} is doing{industry_hints['subject_suffix']}",
+                    "body": f"Hi {name},\n\nJust came across {company}{industry_hints['context']} - really impressive work!\n\nWe've helped similar {industry_hints['industry']} companies {industry_hints['benefit']}. Mind if I share a quick example?\n\n15-minute call work for you?\n\nCheers,\nAlex"
+                },
+                {
+                    "subject": f"Quick idea for {company}",
+                    "body": f"Hi {name},\n\n{company}'s {industry_hints['focus']} caught my eye. We just helped a similar company {industry_hints['specific_result']}.\n\nWorth exploring for {company}?\n\nBest,\nSam"
+                }
+            ]
+        elif tone.lower() == "direct":
+            templates = [
+                {
+                    "subject": f"{company} + {industry_hints['solution']}?",
+                    "body": f"Hi {name},\n\n{industry_hints['direct_opener']} for {company}.\n\nResult: {industry_hints['specific_result']}.\n\nInterested? 10-minute call?\n\n-Alex"
+                },
+                {
+                    "subject": f"ROI opportunity for {company}",
+                    "body": f"{name},\n\nQuick question: Is {company} looking to {industry_hints['goal']}?\n\nWe reduced costs by 35% for a similar {industry_hints['industry']} company.\n\nWorth a conversation?\n\nBest,\nSam"
+                }
+            ]
+        else:  # Professional
+            templates = [
+                {
+                    "subject": f"Operational efficiency opportunity - {company}",
+                    "body": f"Hi {name},\n\nI noticed {company} specializes in {industry_hints['specialty']}. We recently helped a similar organization {industry_hints['professional_result']}.\n\nWould you be open to a brief conversation about how this might apply to {company}?\n\nBest regards,\nAlex Thompson"
+                },
+                {
+                    "subject": f"Thought on {company}'s {industry_hints['focus']}",
+                    "body": f"Hi {name},\n\n{company}'s work in {industry_hints['area']} is impressive. We've developed solutions that help {industry_hints['industry']} companies {industry_hints['benefit']}.\n\nWould you be interested in a 15-minute discussion about potential applications for {company}?\n\nBest regards,\nSarah Chen"
+                }
+            ]
+        
+        template = random.choice(templates)
+        return template["subject"], template["body"]
+    
+    def _extract_industry_details(self, company_info):
+        """Extract industry-specific details for personalization"""
+        info_lower = company_info.lower() if company_info else ""
+        
+        if any(word in info_lower for word in ['tech', 'software', 'saas', 'ai', 'digital']):
+            return {
+                'industry': 'tech',
+                'specialty': 'technology solutions',
+                'focus': 'innovation',
+                'area': 'technology',
+                'benefit': 'scale their platforms and reduce technical debt',
+                'goal': 'optimize your development pipeline',
+                'solution': 'DevOps automation',
+                'context': ' and their tech stack',
+                'subject_suffix': ' with tech',
+                'direct_opener': 'We implemented automated testing',
+                'specific_result': 'reduced deployment time by 60%',
+                'professional_result': 'achieve 40% faster time-to-market for new features'
+            }
+        elif any(word in info_lower for word in ['manufactur', 'industrial', 'equipment', 'materials']):
+            return {
+                'industry': 'manufacturing',
+                'specialty': 'industrial operations',
+                'focus': 'production efficiency',
+                'area': 'manufacturing',
+                'benefit': 'optimize their production lines and reduce waste',
+                'goal': 'increase production efficiency',
+                'solution': 'process optimization',
+                'context': ' and their manufacturing capabilities',
+                'subject_suffix': ' in manufacturing',
+                'direct_opener': 'We streamlined production workflows',
+                'specific_result': 'increased throughput by 45%',
+                'professional_result': 'achieve 30% improvement in production efficiency'
+            }
+        elif any(word in info_lower for word in ['health', 'medical', 'pharma', 'clinical']):
+            return {
+                'industry': 'healthcare',
+                'specialty': 'healthcare solutions',
+                'focus': 'patient outcomes',
+                'area': 'healthcare',
+                'benefit': 'improve patient outcomes while reducing costs',
+                'goal': 'enhance patient care efficiency',
+                'solution': 'workflow optimization',
+                'context': ' and their patient care approach',
+                'subject_suffix': ' in healthcare',
+                'direct_opener': 'We optimized patient flow systems',
+                'specific_result': 'reduced wait times by 50%',
+                'professional_result': 'achieve 25% improvement in patient satisfaction scores'
+            }
+        else:
+            return {
+                'industry': 'business',
+                'specialty': 'business operations',
+                'focus': 'growth',
+                'area': 'operations',
+                'benefit': 'streamline operations and drive growth',
+                'goal': 'scale your operations',
+                'solution': 'process optimization',
+                'context': ' and their business model',
+                'subject_suffix': '',
+                'direct_opener': 'We automated key business processes',
+                'specific_result': 'increased efficiency by 40%',
+                'professional_result': 'achieve 35% operational cost reduction'
+            }
+    
+    def _load_prompt_templates(self):
+        """Load sophisticated prompt templates for different use cases"""
+        return {
+            "few_shot_template": '''You are an elite B2B sales copywriter. Write ONE personalized cold email that sounds natural and converts.
+
+<examples>
+EXAMPLE 1:
+SUBJECT: Quick question about Acme's EU expansion
+BODY: Hi Sarah,
+
+Saw Acme just launched in Berlin – congrats! We helped Contoso reduce their GDPR compliance prep by 68% with a simple automation.
+
+Worth a 10-minute chat about how this could apply to your EU rollout?
+
+Best,
+Alex
+
+EXAMPLE 2:
+SUBJECT: Thought on TechCorp's materials testing
+BODY: Hi John,
+
+Noticed TechCorp specializes in X-ray spectroscopy equipment. We just helped a similar lab increase throughput 40% with workflow optimization.
+
+Mind if I share what worked for them? 15-minute call?
+
+Best,
+Sam
+
+EXAMPLE 3:
+SUBJECT: Manufacturing efficiency idea for IndustrialCorp
+BODY: Hi Mike,
+
+IndustrialCorp's production line setup caught my attention. We automated similar processes for MetalWorks, reducing their cycle time by 35%.
+
+Open to a brief conversation about applications for your facility?
+
+Best regards,
+Jennifer
+</examples>
+
+Now write an email for:
+Name: {name}
+Company: {company}
+Company Info: {company_context}
+Tone: {tone}
+
+Requirements:
+- Use the company info naturally in the first 2 lines
+- Maximum 70 words in body (excluding signature)
+- Clear yes/no question at the end
+- No placeholders like [Your Name] or [Company]
+- Professional but conversational
+- Include specific benefit or result if possible
+
+Return ONLY this JSON format:
+{{"subject": "...", "body": "..."}}''',
+
+            "industry_specific": {
+                "technology": '''Write a cold email for a tech company. Focus on efficiency, scalability, and competitive advantage.''',
+                "healthcare": '''Write a cold email for a healthcare company. Focus on patient outcomes, compliance, and cost reduction.''',
+                "manufacturing": '''Write a cold email for a manufacturing company. Focus on production efficiency, quality, and cost savings.''',
+                "services": '''Write a cold email for a service company. Focus on client satisfaction, process improvement, and growth.''',
+                "default": '''Write a cold email that focuses on business growth and operational efficiency.'''
+            }
+        }
+    
+    def _extract_industry(self, company_info):
+        """Extract industry type from company information"""
+        company_lower = company_info.lower()
+        
+        if any(word in company_lower for word in ['tech', 'software', 'saas', 'ai', 'digital', 'app', 'platform']):
+            return 'technology'
+        elif any(word in company_lower for word in ['health', 'medical', 'pharma', 'hospital', 'clinic']):
+            return 'healthcare'
+        elif any(word in company_lower for word in ['manufactur', 'factory', 'production', 'industrial', 'equipment']):
+            return 'manufacturing'
+        elif any(word in company_lower for word in ['service', 'consulting', 'agency', 'firm']):
+            return 'services'
+        else:
+            return 'default'
+    
+    def _create_company_context(self, company, company_info):
+        """Create focused company context for the prompt"""
+        # Extract key information and clean it up
+        context_parts = []
+        
+        if company_info and len(company_info) > 10:
+            # Extract meaningful phrases
+            sentences = re.split(r'[.!?]+', company_info)
+            for sentence in sentences[:3]:  # First 3 sentences
+                sentence = sentence.strip()
+                if len(sentence) > 20 and not sentence.startswith('Title:'):
+                    # Remove common fluff words
+                    sentence = re.sub(r'Description:\s*', '', sentence)
+                    sentence = re.sub(r'Company Website:\s*', '', sentence)
+                    sentence = re.sub(r'LinkedIn:\s*', '', sentence)
+                    if sentence:
+                        context_parts.append(sentence)
+        
+        if not context_parts:
+            context_parts.append(f"{company} is a company in their industry")
+        
+        return ' | '.join(context_parts[:2])  # Max 2 key points
+    
+    def generate_email(self, name, company, company_info, tone="Professional", temperature=0.7):
+        """Generate both subject and email body using advanced prompting"""
+        # Clean up and prepare context
+        company_context = self._create_company_context(company, company_info)
+        
+        # Calibrate temperature for production readiness
+        if temperature > 1.0:
+            temperature = 0.8  # Cap at 0.8 for production readiness
+        
+        # Try AI generation first
+        if self.model:
+            try:
+                # Build the prompt using few-shot template
+                prompt = self.prompt_templates["few_shot_template"].format(
+                    name=name,
+                    company=company,
+                    company_context=company_context,
+                    tone=tone.lower()
+                )
+                
+                # Generate with model
+                response = self._generate_with_model(prompt, max_tokens=200, temperature=temperature)
+                
+                # Parse the response
+                subject, body = self._parse_json_response(response)
+                
+                # Polish the content
+                subject, body = self._polish_email_content(subject, body)
+                
+                # Validate quality
+                quality_score, issues = self._validate_email_quality(subject, body, name, company)
+                
+                # If quality is good enough, return it
+                if quality_score >= 70 and 'placeholders' not in issues:
+                    print(f"✅ AI generated email (Quality: {quality_score}%)")
+                    return subject, body
+                else:
+                    print(f"⚠️ AI output quality too low ({quality_score}%), using advanced fallback")
+                    
+            except Exception as e:
+                print(f"❌ AI generation failed: {e}, using advanced fallback")
+        
+        # Use advanced fallback system
+        print("🔄 Using advanced fallback generation")
+        subject, body = self._advanced_fallback_generation(name, company, company_info, tone)
+        
+        # Always polish fallback content
+        subject, body = self._polish_email_content(subject, body)
+        
+        return subject, body
+    
+    def _clean_subject(self, subject, company):
+        """Clean and validate subject line"""
+        if not subject or len(subject) < 5:
+            return f"Quick question about {company}"
+        
+        # Remove common prefixes
+        subject = re.sub(r'^(Subject|SUBJECT):\s*', '', subject, flags=re.IGNORECASE)
+        subject = subject.strip('"\'')
+        
+        # Ensure reasonable length
+        if len(subject) > 60:
+            subject = subject[:57] + "..."
+        
+        return subject
+    
+    def _clean_body(self, body, name):
+        """Clean and validate email body"""
+        if not body or len(body) < 20:
+            return f"Hi {name},\n\nI'd love to discuss how we can help your business grow.\n\nInterested in a quick call?\n\nBest regards"
+        
+        # Remove common prefixes
+        body = re.sub(r'^(Body|BODY|Email|EMAIL):\s*', '', body, flags=re.IGNORECASE)
+        
+        # Ensure proper greeting
+        if not body.lower().startswith(('hi ', 'hello ', 'dear ')):
+            body = f"Hi {name},\n\n{body}"
+        
+        # Ensure proper closing
+        closing_patterns = ['best regards', 'best,', 'sincerely', 'regards,', 'cheers,']
+        has_closing = any(pattern in body.lower() for pattern in closing_patterns)
+        
+        if not has_closing:
+            if not body.endswith('\n'):
+                body += '\n'
+            body += '\nBest regards'
+        
+        return body
+    
+    def _polish_email_content(self, subject, body):
+        """Polish email content for grammar and professionalism"""
+        
+        # Fix common grammar issues
+        body = re.sub(r'\s+', ' ', body)  # Multiple spaces
+        body = re.sub(r'([.!?])\s*([a-z])', r'\1 \2', body)  # Space after punctuation
+        body = re.sub(r'(\w)\s*\n\s*(\w)', r'\1\n\n\2', body)  # Proper paragraph spacing
+        
+        # Ensure professional closing
+        if not re.search(r'(Best regards|Best|Sincerely|Cheers),?\s*\n?[A-Z][a-z]+', body):
+            if body.strip().endswith(','):
+                body = body.strip() + '\n\nBest regards,\nAlex'
+            else:
+                body = body.strip() + '\n\nBest regards,\nAlex'
+        
+        # Fix subject line
+        subject = subject.strip()
+        if len(subject) > 65:
+            subject = subject[:62] + "..."
+        
+        # Capitalize first letter of subject if not already
+        if subject and subject[0].islower():
+            subject = subject[0].upper() + subject[1:]
+        
+        return subject, body
+    
+    def _validate_email_quality(self, subject, body, name, company):
+        """Validate email quality and return quality score"""
+        issues = []
+        
+        # Check subject length
+        if len(subject) < 10 or len(subject) > 65:
+            issues.append("subject_length")
+        
+        # Check body length
+        words = len(body.split())
+        if words < 20 or words > 150:
+            issues.append("body_length")
+        
+        # Check for placeholders
+        if '[Your Name]' in body or '[Company]' in body or '{{' in body:
+            issues.append("placeholders")
+        
+        # Check personalization
+        if name not in body or company not in body:
+            issues.append("personalization")
+        
+        # Check for call-to-action
+        cta_phrases = ['call', 'conversation', 'chat', 'discuss', 'talk', 'meeting', 'connect']
+        if not any(phrase in body.lower() for phrase in cta_phrases):
+            issues.append("no_cta")
+        
+        quality_score = max(0, 100 - (len(issues) * 15))
+        return quality_score, issues
+    
+    def generate_multiple_variations(self, name, company, company_info, num_variations=3, tone="Professional"):
+        """Generate multiple email variations with different approaches"""
+        variations = []
+        tones = ["Professional", "Friendly", "Direct"]
+        temperatures = [0.6, 0.7, 0.8]
+        
+        for i in range(num_variations):
+            current_tone = tones[i % len(tones)]
+            current_temp = temperatures[i % len(temperatures)]
+            
+            subject, email_body = self.generate_email(
+                name, company, company_info, 
+                tone=current_tone, temperature=current_temp
+            )
+            
+            variations.append({
+                'variation': i + 1,
+                'tone': current_tone,
+                'temperature': current_temp,
+                'subject': subject,
+                'email_body': email_body
+            })
+        
+        return variations
+    
+    def generate_email_v2(self, recipient_name, recipient_email, company_name, company_data, tone="professional", temperature=0.7):
+        """Compatibility method for different calling signatures"""
+        # Extract company info from company_data if it's a dict
+        if isinstance(company_data, dict):
+            company_info = company_data.get('description', f"Company: {company_name}")
+        else:
+            company_info = str(company_data) if company_data else f"Company: {company_name}"
+        
+        # Call the main generate_email method
+        subject, body = self.generate_email(
+            name=recipient_name,
+            company=company_name,
+            company_info=company_info,
+            tone=tone,
+            temperature=temperature
+        )
+        
+        # Return in the expected format
+        return {
+            'subject': subject,
+            'content': body,
+            'quality_score': 8.0
+        }