Update services/pipeline_generator.py

services/pipeline_generator.py

@@ -1,410 +1,410 @@
-# services/pipeline_generator.py
-"""
-Unified pipeline generator with Bedrock (priority) and Gemini (fallback)
-"""
-import json
-import os
-import re
-from typing import Dict, Any, List, Optional
-from pydantic import BaseModel, Field
-
-# For Bedrock
-try:
-    from langchain_aws import ChatBedrock
-    from langchain_core.prompts import ChatPromptTemplate
-    BEDROCK_AVAILABLE = True
-except ImportError:
-    BEDROCK_AVAILABLE = False
-    print("Warning: langchain_aws not available, Bedrock will be disabled")
-
-# For Gemini
-import requests
-
-
-# ========================
-# PYDANTIC MODELS
-# ========================
-
-class ComponentConfig(BaseModel):
-    """Configuration for a single pipeline component"""
-    tool_name: str = Field(description="Name of the tool to execute")
-    start_page: int = Field(default=1, description="Starting page number (1-indexed)")
-    end_page: int = Field(default=1, description="Ending page number (inclusive)")
-    params: Dict[str, Any] = Field(default_factory=dict, description="Additional tool-specific parameters")
-    
-class PipelineConfig(BaseModel):
-    """Complete pipeline configuration"""
-    pipeline_name: str = Field(description="Name/identifier for the pipeline")
-    components: List[ComponentConfig] = Field(description="Ordered list of components to execute")
-    target_lang: Optional[str] = Field(default=None, description="Target language for translation (if applicable)")
-    reason: str = Field(description="AI's reasoning for this pipeline structure")
-    metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata")
-
-
-# ========================
-# BEDROCK PIPELINE GENERATOR
-# ========================
-
-def generate_pipeline_bedrock(user_input: str, file_path: Optional[str] = None) -> Dict[str, Any]:
-    """
-    Generate pipeline using AWS Bedrock (Claude 3.5 Sonnet)
-    Priority method - tries this first
-    """
-    if not BEDROCK_AVAILABLE:
-        raise RuntimeError("Bedrock not available - langchain_aws not installed")
-    
-    # Check for AWS credentials
-    if not os.getenv("AWS_ACCESS_KEY_ID") or not os.getenv("AWS_SECRET_ACCESS_KEY"):
-        raise RuntimeError("AWS credentials not configured")
-    
-    try:
-        llm = ChatBedrock(
-            model_id=os.getenv("BEDROCK_MODEL", "anthropic.claude-3-5-sonnet-20241022-v2:0"),
-            region_name=os.getenv("AWS_REGION", "us-east-1"),
-            temperature=0.0,
-        )
-        
-        prompt = ChatPromptTemplate.from_messages([
-            ("system", """You are a document processing pipeline expert. Generate a detailed pipeline plan.
-
-Available tools and their parameters:
-1. extract_text - Extract text from documents
-   - start_page (int): Starting page number
-   - end_page (int): Ending page number
-   - params: {{"encoding": "utf-8", "preserve_layout": bool}}
-
-2. extract_tables - Extract tables from documents
-   - start_page (int): Starting page number
-   - end_page (int): Ending page number
-   - params: {{"format": "json"|"csv", "include_headers": bool}}
-
-3. describe_images - Generate image descriptions
-   - start_page (int): Starting page number
-   - end_page (int): Ending page number
-   - params: {{"detail_level": "low"|"medium"|"high"}}
-
-4. summarize_text - Summarize extracted text
-   - No page range (works on extracted text)
-   - params: {{"max_length": int, "style": "concise"|"detailed"}}
-
-5. classify_text - Classify document content
-   - No page range (works on extracted text)
-   - params: {{"categories": list[str]}}
-
-6. extract_entities - Named Entity Recognition
-   - No page range (works on extracted text)
-   - params: {{"entity_types": list[str]}}
-
-7. translate_text - Translate text to target language
-   - No page range (works on extracted text)
-   - params: {{"target_lang": str, "source_lang": str}}
-
-8. signature_verification - Verify signatures
-   - start_page (int): Starting page number
-   - end_page (int): Ending page number
-   - params: {{}}
-
-9. stamp_detection - Detect stamps
-   - start_page (int): Starting page number
-   - end_page (int): Ending page number
-   - params: {{}}
-
-Return ONLY valid JSON in this EXACT format:
-{{
-  "pipeline_name": "descriptive-name",
-  "components": [
-    {{
-      "tool_name": "extract_text",
-      "start_page": 1,
-      "end_page": 5,
-      "params": {{"encoding": "utf-8"}}
-    }},
-    {{
-      "tool_name": "summarize_text",
-      "start_page": 1,
-      "end_page": 1,
-      "params": {{"max_length": 500}}
-    }}
-  ],
-  "target_lang": null,
-  "reason": "Brief explanation of why this pipeline",
-  "metadata": {{
-    "estimated_duration_seconds": 30
-  }}
-}}
-
-IMPORTANT:
-- For text processing tools (summarize, classify, NER, translate): start_page=1, end_page=1
-- For document extraction tools: use actual page ranges from user request
-- Components execute in ORDER - ensure dependencies are met
-- Always include "reason" explaining the pipeline choice"""),
-            ("human", "User request: {input}\n\nFile: {file_path}")
-        ])
-        
-        chain = prompt | llm
-        response = chain.invoke({
-            "input": user_input,
-            "file_path": file_path or "user uploaded document"
-        })
-        
-        # Parse JSON from response
-        content = response.content
-        
-        # Try direct JSON parse
-        try:
-            pipeline = json.loads(content)
-        except json.JSONDecodeError:
-            # Extract JSON from markdown code blocks
-            json_match = re.search(r'```json\s*(\{.*?\})\s*```', content, re.DOTALL)
-            if json_match:
-                pipeline = json.loads(json_match.group(1))
-            else:
-                # Try to find any JSON object
-                json_match = re.search(r'\{.*\}', content, re.DOTALL)
-                if json_match:
-                    pipeline = json.loads(json_match.group(0))
-                else:
-                    raise ValueError(f"No JSON found in Bedrock response: {content}")
-        
-        # Add generator metadata
-        pipeline["_generator"] = "bedrock"
-        pipeline["_model"] = os.getenv("BEDROCK_MODEL", "anthropic.claude-3-5-sonnet-20241022-v2:0")
-        
-        # Validate with Pydantic
-        validated = PipelineConfig(**pipeline)
-        
-        return validated.model_dump()
-        
-    except Exception as e:
-        raise RuntimeError(f"Bedrock pipeline generation failed: {str(e)}")
-
-
-# ========================
-# GEMINI PIPELINE GENERATOR
-# ========================
-
-def generate_pipeline_gemini(user_input: str, file_path: Optional[str] = None) -> Dict[str, Any]:
-    """
-    Generate pipeline using Google Gemini (fallback method)
-    """
-    GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")
-    GEMINI_MODEL = os.getenv("GEMINI_MODEL", "gemini-2.0-flash")
-    GEMINI_ENDPOINT = f"https://generativelanguage.googleapis.com/v1beta/models/{GEMINI_MODEL}:generateContent"
-    
-    if not GEMINI_API_KEY:
-        raise RuntimeError("Gemini API key not configured")
-    
-    prompt = f"""You are a document processing pipeline expert. Generate a detailed pipeline plan.
-
-Available tools and their parameters:
-- extract_text: start_page, end_page, params
-- extract_tables: start_page, end_page, params
-- describe_images: start_page, end_page, params
-- summarize_text: params (no page range)
-- classify_text: params (no page range)
-- extract_entities: params (no page range)
-- translate_text: params with target_lang (no page range)
-- signature_verification: start_page, end_page
-- stamp_detection: start_page, end_page
-
-User request: {user_input}
-File: {file_path or "user uploaded document"}
-
-Return ONLY valid JSON in this format:
-{{
-  "pipeline_name": "descriptive-name",
-  "components": [
-    {{
-      "tool_name": "extract_text",
-      "start_page": 1,
-      "end_page": 5,
-      "params": {{}}
-    }}
-  ],
-  "target_lang": null,
-  "reason": "explanation",
-  "metadata": {{"estimated_duration_seconds": 30}}
-}}"""
-    
-    try:
-        response = requests.post(
-            f"{GEMINI_ENDPOINT}?key={GEMINI_API_KEY}",
-            headers={"Content-Type": "application/json"},
-            json={
-                "contents": [{"parts": [{"text": prompt}]}],
-                "generationConfig": {
-                    "temperature": 0.0,
-                    "maxOutputTokens": 1024,
-                }
-            },
-            timeout=60,
-        )
-        
-        response.raise_for_status()
-        result = response.json()
-        
-        # Extract text from Gemini response
-        content = result["candidates"][0]["content"]["parts"][0]["text"]
-        
-        # Parse JSON
-        try:
-            pipeline = json.loads(content)
-        except json.JSONDecodeError:
-            # Extract from code blocks
-            json_match = re.search(r'```json\s*(\{.*?\})\s*```', content, re.DOTALL)
-            if json_match:
-                pipeline = json.loads(json_match.group(1))
-            else:
-                json_match = re.search(r'\{.*\}', content, re.DOTALL)
-                pipeline = json.loads(json_match.group(0))
-        
-        # Add generator metadata
-        pipeline["_generator"] = "gemini"
-        pipeline["_model"] = GEMINI_MODEL
-        
-        # Validate with Pydantic
-        validated = PipelineConfig(**pipeline)
-        
-        return validated.model_dump()
-        
-    except Exception as e:
-        raise RuntimeError(f"Gemini pipeline generation failed: {str(e)}")
-
-
-# ========================
-# UNIFIED PIPELINE GENERATOR WITH FALLBACK
-# ========================
-
-def generate_pipeline(
-    user_input: str,
-    file_path: Optional[str] = None,
-    prefer_bedrock: bool = True
-) -> Dict[str, Any]:
-    """
-    Generate pipeline with fallback mechanism.
-    
-    Priority:
-    1. Try Bedrock (Claude 3.5 Sonnet) - if available and configured
-    2. Fallback to Gemini - if Bedrock fails
-    
-    Returns:
-        Pipeline configuration dict with component-level details
-    """
-    errors = []
-    
-    # Try Bedrock first (priority)
-    if prefer_bedrock and BEDROCK_AVAILABLE:
-        try:
-            print("🏆 Attempting pipeline generation with Bedrock...")
-            pipeline = generate_pipeline_bedrock(user_input, file_path)
-            print(f"✅ Bedrock pipeline generated successfully: {pipeline['pipeline_name']}")
-            return pipeline
-        except Exception as bedrock_error:
-            error_msg = f"Bedrock failed: {str(bedrock_error)}"
-            print(f"❌ {error_msg}")
-            errors.append(error_msg)
-            print("🔄 Falling back to Gemini...")
-    
-    # Fallback to Gemini
-    try:
-        print("🔄 Attempting pipeline generation with Gemini...")
-        pipeline = generate_pipeline_gemini(user_input, file_path)
-        print(f"✅ Gemini pipeline generated successfully: {pipeline['pipeline_name']}")
-        
-        # Add fallback metadata
-        if errors:
-            if "metadata" not in pipeline:
-                pipeline["metadata"] = {}
-            pipeline["metadata"]["fallback_reason"] = errors[0]
-        
-        return pipeline
-    except Exception as gemini_error:
-        error_msg = f"Gemini failed: {str(gemini_error)}"
-        print(f"❌ {error_msg}")
-        errors.append(error_msg)
-    
-    # Both failed
-    raise RuntimeError(
-        f"Pipeline generation failed with all providers.\n"
-        f"Errors:\n" + "\n".join(f"  - {e}" for e in errors)
-    )
-
-
-# ========================
-# UTILITY FUNCTIONS
-# ========================
-
-def format_pipeline_for_display(pipeline: Dict[str, Any]) -> str:
-    """
-    Format pipeline as fancy display string for Gradio
-    """
-    generator = pipeline.get("_generator", "unknown")
-    model = pipeline.get("_model", "unknown")
-    
-    display = f"""
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-🎯 PIPELINE GENERATED SUCCESSFULLY!
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-📋 Pipeline Name: {pipeline.get('pipeline_name', 'unnamed')}
-🤖 Generated By: {generator.title()} ({model})
-⏱️  Estimated Duration: {pipeline.get('metadata', {}).get('estimated_duration_seconds', 'unknown')} seconds
-
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-"""
-    
-    # Add each component
-    for idx, component in enumerate(pipeline.get("components", []), 1):
-        tool_name = component.get("tool_name", "unknown")
-        start_page = component.get("start_page", 1)
-        end_page = component.get("end_page", 1)
-        params = component.get("params", {})
-        
-        # Icon based on tool type
-        icon = {
-            "extract_text": "📄",
-            "extract_tables": "📊",
-            "describe_images": "🖼️",
-            "summarize_text": "📝",
-            "classify_text": "🏷️",
-            "extract_entities": "👤",
-            "translate_text": "🌐",
-            "signature_verification": "✍️",
-            "stamp_detection": "🔖"
-        }.get(tool_name, "🔧")
-        
-        display += f"\n{icon} **STEP {idx}: {tool_name.replace('_', ' ').upper()}**\n"
-        
-        if start_page > 1 or end_page > 1:
-            display += f"   📍 Pages: {start_page} to {end_page}\n"
-        
-        if params:
-            display += "   ⚙️  Parameters:\n"
-            for key, value in params.items():
-                display += f"      • {key}: {value}\n"
-        
-        display += "\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
-    
-    # Add reasoning
-    display += f"\n💡 **REASONING:**\n   {pipeline.get('reason', 'No reason provided')}\n"
-    
-    display += "\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
-    display += "\n✅ Type 'approve' to execute this pipeline"
-    display += "\n❌ Type 'reject' to cancel"
-    display += "\n✏️  Type 'edit' to modify\n"
-    display += "\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-    
-    return display
-
-
-if __name__ == "__main__":
-    # Test
-    test_input = "extract text from pages 1-5, get tables from pages 2-4, and summarize everything"
-    
-    try:
-        pipeline = generate_pipeline(test_input)
-        print(json.dumps(pipeline, indent=2))
-        print("\n" + "="*80 + "\n")
-        print(format_pipeline_for_display(pipeline))
-    except Exception as e:
-        print(f"Error: {e}")
+# services/pipeline_generator.py
+"""
+Unified pipeline generator with Bedrock (priority) and Gemini (fallback)
+"""
+import json
+import os
+import re
+from typing import Dict, Any, List, Optional
+from pydantic import BaseModel, Field
+
+# For Bedrock
+try:
+    from langchain_aws import ChatBedrock
+    from langchain_core.prompts import ChatPromptTemplate
+    BEDROCK_AVAILABLE = True
+except ImportError:
+    BEDROCK_AVAILABLE = False
+    print("Warning: langchain_aws not available, Bedrock will be disabled")
+
+# For Gemini
+import requests
+
+
+# ========================
+# PYDANTIC MODELS
+# ========================
+
+class ComponentConfig(BaseModel):
+    """Configuration for a single pipeline component"""
+    tool_name: str = Field(description="Name of the tool to execute")
+    start_page: int = Field(default=1, description="Starting page number (1-indexed)")
+    end_page: int = Field(default=1, description="Ending page number (inclusive)")
+    params: Dict[str, Any] = Field(default_factory=dict, description="Additional tool-specific parameters")
+    
+class PipelineConfig(BaseModel):
+    """Complete pipeline configuration"""
+    pipeline_name: str = Field(description="Name/identifier for the pipeline")
+    components: List[ComponentConfig] = Field(description="Ordered list of components to execute")
+    target_lang: Optional[str] = Field(default=None, description="Target language for translation (if applicable)")
+    reason: str = Field(description="AI's reasoning for this pipeline structure")
+    metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata")
+
+
+# ========================
+# BEDROCK PIPELINE GENERATOR
+# ========================
+
+def generate_pipeline_bedrock(user_input: str, file_path: Optional[str] = None) -> Dict[str, Any]:
+    """
+    Generate pipeline using AWS Bedrock (Mistral Large)
+    Priority method - tries this first
+    """
+    if not BEDROCK_AVAILABLE:
+        raise RuntimeError("Bedrock not available - langchain_aws not installed")
+    
+    # Check for AWS credentials
+    if not os.getenv("AWS_ACCESS_KEY_ID") or not os.getenv("AWS_SECRET_ACCESS_KEY"):
+        raise RuntimeError("AWS credentials not configured")
+    
+    try:
+        llm = ChatBedrock(
+            model_id="mistral.mistral-large-2402-v1:0",
+            region_name=os.getenv("AWS_REGION", "us-east-1"),
+            temperature=0.0,
+        )
+        
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", """You are a document processing pipeline expert. Generate a detailed pipeline plan.
+
+Available tools and their parameters:
+1. extract_text - Extract text from documents
+   - start_page (int): Starting page number
+   - end_page (int): Ending page number
+   - params: {{"encoding": "utf-8", "preserve_layout": bool}}
+
+2. extract_tables - Extract tables from documents
+   - start_page (int): Starting page number
+   - end_page (int): Ending page number
+   - params: {{"format": "json"|"csv", "include_headers": bool}}
+
+3. describe_images - Generate image descriptions
+   - start_page (int): Starting page number
+   - end_page (int): Ending page number
+   - params: {{"detail_level": "low"|"medium"|"high"}}
+
+4. summarize_text - Summarize extracted text
+   - No page range (works on extracted text)
+   - params: {{"max_length": int, "style": "concise"|"detailed"}}
+
+5. classify_text - Classify document content
+   - No page range (works on extracted text)
+   - params: {{"categories": list[str]}}
+
+6. extract_entities - Named Entity Recognition
+   - No page range (works on extracted text)
+   - params: {{"entity_types": list[str]}}
+
+7. translate_text - Translate text to target language
+   - No page range (works on extracted text)
+   - params: {{"target_lang": str, "source_lang": str}}
+
+8. signature_verification - Verify signatures
+   - start_page (int): Starting page number
+   - end_page (int): Ending page number
+   - params: {{}}
+
+9. stamp_detection - Detect stamps
+   - start_page (int): Starting page number
+   - end_page (int): Ending page number
+   - params: {{}}
+
+Return ONLY valid JSON in this EXACT format:
+{{
+  "pipeline_name": "descriptive-name",
+  "components": [
+    {{
+      "tool_name": "extract_text",
+      "start_page": 1,
+      "end_page": 5,
+      "params": {{"encoding": "utf-8"}}
+    }},
+    {{
+      "tool_name": "summarize_text",
+      "start_page": 1,
+      "end_page": 1,
+      "params": {{"max_length": 500}}
+    }}
+  ],
+  "target_lang": null,
+  "reason": "Brief explanation of why this pipeline",
+  "metadata": {{
+    "estimated_duration_seconds": 30
+  }}
+}}
+
+IMPORTANT:
+- For text processing tools (summarize, classify, NER, translate): start_page=1, end_page=1
+- For document extraction tools: use actual page ranges from user request
+- Components execute in ORDER - ensure dependencies are met
+- Always include "reason" explaining the pipeline choice"""),
+            ("human", "User request: {input}\n\nFile: {file_path}")
+        ])
+        
+        chain = prompt | llm
+        response = chain.invoke({
+            "input": user_input,
+            "file_path": file_path or "user uploaded document"
+        })
+        
+        # Parse JSON from response
+        content = response.content
+        
+        # Try direct JSON parse
+        try:
+            pipeline = json.loads(content)
+        except json.JSONDecodeError:
+            # Extract JSON from markdown code blocks
+            json_match = re.search(r'```json\s*(\{.*?\})\s*```', content, re.DOTALL)
+            if json_match:
+                pipeline = json.loads(json_match.group(1))
+            else:
+                # Try to find any JSON object
+                json_match = re.search(r'\{.*\}', content, re.DOTALL)
+                if json_match:
+                    pipeline = json.loads(json_match.group(0))
+                else:
+                    raise ValueError(f"No JSON found in Bedrock response: {content}")
+        
+        # Add generator metadata
+        pipeline["_generator"] = "bedrock"
+        pipeline["_model"] = "mistral.mistral-large-2402-v1:0"
+        
+        # Validate with Pydantic
+        validated = PipelineConfig(**pipeline)
+        
+        return validated.model_dump()
+        
+    except Exception as e:
+        raise RuntimeError(f"Bedrock pipeline generation failed: {str(e)}")
+
+
+# ========================
+# GEMINI PIPELINE GENERATOR
+# ========================
+
+def generate_pipeline_gemini(user_input: str, file_path: Optional[str] = None) -> Dict[str, Any]:
+    """
+    Generate pipeline using Google Gemini (fallback method)
+    """
+    GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")
+    GEMINI_MODEL = os.getenv("GEMINI_MODEL", "gemini-2.0-flash")
+    GEMINI_ENDPOINT = f"https://generativelanguage.googleapis.com/v1beta/models/{GEMINI_MODEL}:generateContent"
+    
+    if not GEMINI_API_KEY:
+        raise RuntimeError("Gemini API key not configured")
+    
+    prompt = f"""You are a document processing pipeline expert. Generate a detailed pipeline plan.
+
+Available tools and their parameters:
+- extract_text: start_page, end_page, params
+- extract_tables: start_page, end_page, params
+- describe_images: start_page, end_page, params
+- summarize_text: params (no page range)
+- classify_text: params (no page range)
+- extract_entities: params (no page range)
+- translate_text: params with target_lang (no page range)
+- signature_verification: start_page, end_page
+- stamp_detection: start_page, end_page
+
+User request: {user_input}
+File: {file_path or "user uploaded document"}
+
+Return ONLY valid JSON in this format:
+{{
+  "pipeline_name": "descriptive-name",
+  "components": [
+    {{
+      "tool_name": "extract_text",
+      "start_page": 1,
+      "end_page": 5,
+      "params": {{}}
+    }}
+  ],
+  "target_lang": null,
+  "reason": "explanation",
+  "metadata": {{"estimated_duration_seconds": 30}}
+}}"""
+    
+    try:
+        response = requests.post(
+            f"{GEMINI_ENDPOINT}?key={GEMINI_API_KEY}",
+            headers={"Content-Type": "application/json"},
+            json={
+                "contents": [{"parts": [{"text": prompt}]}],
+                "generationConfig": {
+                    "temperature": 0.0,
+                    "maxOutputTokens": 1024,
+                }
+            },
+            timeout=60,
+        )
+        
+        response.raise_for_status()
+        result = response.json()
+        
+        # Extract text from Gemini response
+        content = result["candidates"][0]["content"]["parts"][0]["text"]
+        
+        # Parse JSON
+        try:
+            pipeline = json.loads(content)
+        except json.JSONDecodeError:
+            # Extract from code blocks
+            json_match = re.search(r'```json\s*(\{.*?\})\s*```', content, re.DOTALL)
+            if json_match:
+                pipeline = json.loads(json_match.group(1))
+            else:
+                json_match = re.search(r'\{.*\}', content, re.DOTALL)
+                pipeline = json.loads(json_match.group(0))
+        
+        # Add generator metadata
+        pipeline["_generator"] = "gemini"
+        pipeline["_model"] = GEMINI_MODEL
+        
+        # Validate with Pydantic
+        validated = PipelineConfig(**pipeline)
+        
+        return validated.model_dump()
+        
+    except Exception as e:
+        raise RuntimeError(f"Gemini pipeline generation failed: {str(e)}")
+
+
+# ========================
+# UNIFIED PIPELINE GENERATOR WITH FALLBACK
+# ========================
+
+def generate_pipeline(
+    user_input: str,
+    file_path: Optional[str] = None,
+    prefer_bedrock: bool = True
+) -> Dict[str, Any]:
+    """
+    Generate pipeline with fallback mechanism.
+    
+    Priority:
+    1. Try Bedrock (Mistral Large) - if available and configured
+    2. Fallback to Gemini - if Bedrock fails
+    
+    Returns:
+        Pipeline configuration dict with component-level details
+    """
+    errors = []
+    
+    # Try Bedrock first (priority)
+    if prefer_bedrock and BEDROCK_AVAILABLE:
+        try:
+            print("🏆 Attempting pipeline generation with Bedrock...")
+            pipeline = generate_pipeline_bedrock(user_input, file_path)
+            print(f"✅ Bedrock pipeline generated successfully: {pipeline['pipeline_name']}")
+            return pipeline
+        except Exception as bedrock_error:
+            error_msg = f"Bedrock failed: {str(bedrock_error)}"
+            print(f"❌ {error_msg}")
+            errors.append(error_msg)
+            print("🔄 Falling back to Gemini...")
+    
+    # Fallback to Gemini
+    try:
+        print("🔄 Attempting pipeline generation with Gemini...")
+        pipeline = generate_pipeline_gemini(user_input, file_path)
+        print(f"✅ Gemini pipeline generated successfully: {pipeline['pipeline_name']}")
+        
+        # Add fallback metadata
+        if errors:
+            if "metadata" not in pipeline:
+                pipeline["metadata"] = {}
+            pipeline["metadata"]["fallback_reason"] = errors[0]
+        
+        return pipeline
+    except Exception as gemini_error:
+        error_msg = f"Gemini failed: {str(gemini_error)}"
+        print(f"❌ {error_msg}")
+        errors.append(error_msg)
+    
+    # Both failed
+    raise RuntimeError(
+        f"Pipeline generation failed with all providers.\n"
+        f"Errors:\n" + "\n".join(f"  - {e}" for e in errors)
+    )
+
+
+# ========================
+# UTILITY FUNCTIONS
+# ========================
+
+def format_pipeline_for_display(pipeline: Dict[str, Any]) -> str:
+    """
+    Format pipeline as fancy display string for Gradio
+    """
+    generator = pipeline.get("_generator", "unknown")
+    model = pipeline.get("_model", "unknown")
+    
+    display = f"""
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+🎯 PIPELINE GENERATED SUCCESSFULLY!
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+📋 Pipeline Name: {pipeline.get('pipeline_name', 'unnamed')}
+🤖 Generated By: {generator.title()} ({model})
+⏱️  Estimated Duration: {pipeline.get('metadata', {}).get('estimated_duration_seconds', 'unknown')} seconds
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+"""
+    
+    # Add each component
+    for idx, component in enumerate(pipeline.get("components", []), 1):
+        tool_name = component.get("tool_name", "unknown")
+        start_page = component.get("start_page", 1)
+        end_page = component.get("end_page", 1)
+        params = component.get("params", {})
+        
+        # Icon based on tool type
+        icon = {
+            "extract_text": "📄",
+            "extract_tables": "📊",
+            "describe_images": "🖼️",
+            "summarize_text": "📝",
+            "classify_text": "🏷️",
+            "extract_entities": "👤",
+            "translate_text": "🌐",
+            "signature_verification": "✍️",
+            "stamp_detection": "🔖"
+        }.get(tool_name, "🔧")
+        
+        display += f"\n{icon} **STEP {idx}: {tool_name.replace('_', ' ').upper()}**\n"
+        
+        if start_page > 1 or end_page > 1:
+            display += f"   📍 Pages: {start_page} to {end_page}\n"
+        
+        if params:
+            display += "   ⚙️  Parameters:\n"
+            for key, value in params.items():
+                display += f"      • {key}: {value}\n"
+        
+        display += "\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
+    
+    # Add reasoning
+    display += f"\n💡 **REASONING:**\n   {pipeline.get('reason', 'No reason provided')}\n"
+    
+    display += "\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n"
+    display += "\n✅ Type 'approve' to execute this pipeline"
+    display += "\n❌ Type 'reject' to cancel"
+    display += "\n✏️  Type 'edit' to modify\n"
+    display += "\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+    
+    return display
+
+
+if __name__ == "__main__":
+    # Test
+    test_input = "extract text from pages 1-5, get tables from pages 2-4, and summarize everything"
+    
+    try:
+        pipeline = generate_pipeline(test_input)
+        print(json.dumps(pipeline, indent=2))
+        print("\n" + "="*80 + "\n")
+        print(format_pipeline_for_display(pipeline))
+    except Exception as e:
+        print(f"Error: {e}")