Add cahrtgemma srvice

UML_Diagrams.md

+# ChartAnalyzer System - UML Diagrams
+
+## 1. Use Case Diagram
+
+```mermaid
+graph TB
+    subgraph "ChartAnalyzer System"
+        subgraph "Actors"
+            User[👤 User]
+            Admin[👨‍💼 Admin]
+            LLMService[🤖 LLM Service]
+        end
+        
+        subgraph "Authentication Use Cases"
+            UC1[Register Account]
+            UC2[Login]
+            UC3[Logout]
+            UC4[Validate Token]
+        end
+        
+        subgraph "Chart Analysis Use Cases"
+            UC5[Upload Chart Image]
+            UC6[Analyze Chart General]
+            UC7[Ask Specific Question]
+            UC8[Get Advanced Analysis]
+            UC9[View Analysis History]
+        end
+        
+        subgraph "System Management Use Cases"
+            UC10[Check Server Status]
+            UC11[List Available Models]
+            UC12[Test Model Connection]
+        end
+    end
+    
+    %% Actor relationships
+    User --> UC1
+    User --> UC2
+    User --> UC3
+    User --> UC5
+    User --> UC6
+    User --> UC7
+    User --> UC8
+    User --> UC9
+    User --> UC10
+    User --> UC11
+    User --> UC12
+    
+    Admin --> UC10
+    Admin --> UC11
+    Admin --> UC12
+    
+    %% Include relationships
+    UC6 -.->|includes| UC5
+    UC7 -.->|includes| UC5
+    UC8 -.->|includes| UC5
+    
+    %% Extend relationships
+    UC8 -.->|extends| UC7
+    
+    %% System dependencies
+    UC6 -.->|uses| LLMService
+    UC7 -.->|uses| LLMService
+    UC8 -.->|uses| LLMService
+```
+
+## 2. System Sequence Diagrams
+
+### 2.1 User Registration Flow
+
+```mermaid
+sequenceDiagram
+    participant U as User
+    participant F as Frontend
+    participant API as FastAPI
+    participant AS as AuthService
+    participant UR as UserRepository
+    participant DB as Database
+    
+    U->>F: Fill registration form
+    F->>API: POST /auth/register
+    API->>AS: register(email, password)
+    AS->>AS: hash_password(password)
+    AS->>UR: create_user(user)
+    UR->>DB: INSERT user
+    DB-->>UR: user_id
+    UR-->>AS: User object
+    AS-->>API: User object
+    API-->>F: 201 Created
+    F-->>U: Registration success
+```
+
+### 2.2 Chart Analysis Flow
+
+```mermaid
+sequenceDiagram
+    participant U as User
+    participant F as Frontend
+    participant API as FastAPI
+    participant CS as ChartService
+    participant LS as LLMService
+    participant CG as ChartGemma
+    participant O as Ollama
+    
+    U->>F: Upload image + question
+    F->>API: POST /charts/analyze
+    API->>CS: analyze_chart(image, question)
+    CS->>LS: analyze(request)
+    alt Model Selection
+        LS->>CG: analyze(image, question)
+        CG-->>LS: analysis_result
+    else Default Model
+        LS->>O: analyze(image, question)
+        O-->>LS: analysis_result
+    end
+    LS-->>CS: LLMResponse
+    CS-->>API: Analysis result
+    API-->>F: 200 OK
+    F-->>U: Display analysis
+```
+
+### 2.3 Advanced Chart Analysis Flow
+
+```mermaid
+sequenceDiagram
+    participant U as User
+    participant F as Frontend
+    participant API as FastAPI
+    participant CS as ChartService
+    participant CG as ChartGemma
+    
+    U->>F: Upload image + question + advanced=true
+    F->>API: POST /charts/ask
+    API->>CS: analyze_specialized(image, question, advanced)
+    CS->>CS: Add "PROGRAM OF THOUGHT" prefix
+    CS->>CG: analyze(enhanced_question)
+    CG-->>CS: Advanced analysis result
+    CS-->>API: Analysis result
+    API-->>F: 200 OK
+    F-->>U: Display advanced analysis
+```
+
+## 3. Class Diagram
+
+```mermaid
+classDiagram
+    %% Domain Entities
+    class User {
+        +String id
+        +EmailStr email
+        +String password_hash
+        +Boolean is_active
+        +DateTime created_at
+        +DateTime last_login
+    }
+    
+    class ChartImage {
+        +String id
+        +String user_id
+        +Bytes image_data
+        +DateTime uploaded_at
+    }
+    
+    class ChartAnalysis {
+        +String id
+        +String chart_image_id
+        +String question
+        +String answer
+        +DateTime created_at
+    }
+    
+    %% Application DTOs
+    class RegisterRequestDTO {
+        +String email
+        +String password
+    }
+    
+    class LoginRequestDTO {
+        +String email
+        +String password
+    }
+    
+    class LLMRequestDTO {
+        +Bytes image_bytes
+        +String question
+    }
+    
+    class LLMResponseDTO {
+        +String answer
+        +String model_used
+        +Float processing_time
+    }
+    
+    %% Application Services
+    class AuthService {
+        -UserRepositoryPort user_repo
+        -TokenRepositoryPort token_repo
+        -String secret_key
+        +register(email, password) User
+        +login(email, password) String
+        +logout(token) void
+        +validate_token(token) Boolean
+    }
+    
+    class ChartGemmaService {
+        -String gradio_url
+        +analyze(request) LLMResponseDTO
+        -_load_image(image_data) Image
+        -_call_gradio_api(image, question) String
+    }
+    
+    class OllamaService {
+        -String host
+        -String model
+        +analyze(request) LLMResponseDTO
+    }
+    
+    %% Domain Ports (Interfaces)
+    class UserRepositoryPort {
+        <<interface>>
+        +create_user(user) void
+        +get_by_email(email) User
+        +get_by_id(id) User
+    }
+    
+    class ChartsRepositoryPort {
+        <<interface>>
+        +save(chart_image) void
+        +get_by_id(id) ChartImage
+        +get_by_user_id(user_id) List~ChartImage~
+    }
+    
+    class LLMServicePort {
+        <<interface>>
+        +analyze(request) LLMResponseDTO
+    }
+    
+    %% Use Cases
+    class UploadChartUseCase {
+        -ChartsRepositoryPort image_repo
+        +execute(image_bytes, user_id) String
+    }
+    
+    class AnalyzeChartUseCase {
+        -ChartsRepositoryPort charts_repo
+        -AnalysisRepositoryPort analysis_repo
+        +execute(image_bytes, question) ChartAnalysis
+    }
+    
+    %% Relationships
+    User ||--o{ ChartImage : owns
+    ChartImage ||--o{ ChartAnalysis : generates
+    AuthService ..|> UserRepositoryPort : uses
+    ChartGemmaService ..|> LLMServicePort : implements
+    OllamaService ..|> LLMServicePort : implements
+    UploadChartUseCase ..|> ChartsRepositoryPort : uses
+    AnalyzeChartUseCase ..|> ChartsRepositoryPort : uses
+    AnalyzeChartUseCase ..|> AnalysisRepositoryPort : uses
+```
+
+## 4. Component Diagram
+
+```mermaid
+graph TB
+    subgraph "Frontend Layer (React + TypeScript)"
+        UI[User Interface]
+        AC[Auth Context]
+        CS[Chart Service]
+        AS[API Service]
+    end
+    
+    subgraph "API Gateway Layer (FastAPI)"
+        Router[Router]
+        Middleware[CORS Middleware]
+        Auth[Authentication]
+    end
+    
+    subgraph "Application Layer"
+        UC[Use Cases]
+        AServices[Application Services]
+        DTOs[Data Transfer Objects]
+    end
+    
+    subgraph "Domain Layer"
+        Entities[Domain Entities]
+        Ports[Domain Ports]
+        Services[Domain Services]
+    end
+    
+    subgraph "Infrastructure Layer"
+        Repos[Repositories]
+        External[External Services]
+        DB[(PostgreSQL)]
+    end
+    
+    subgraph "External Services"
+        ChartGemma[ChartGemma Model]
+        Ollama[Ollama LLM]
+        Gradio[Gradio API]
+    end
+    
+    %% Frontend connections
+    UI --> AC
+    UI --> CS
+    CS --> AS
+    
+    %% API connections
+    AS --> Router
+    Router --> Middleware
+    Router --> Auth
+    
+    %% Application connections
+    Router --> UC
+    UC --> AServices
+    AServices --> DTOs
+    
+    %% Domain connections
+    AServices --> Entities
+    AServices --> Ports
+    Ports --> Services
+    
+    %% Infrastructure connections
+    Ports --> Repos
+    Repos --> DB
+    Services --> External
+    
+    %% External service connections
+    External --> ChartGemma
+    External --> Ollama
+    External --> Gradio
+```
+
+## 5. Activity Diagram - Chart Analysis Process
+
+```mermaid
+flowchart TD
+    Start([User starts analysis]) --> Upload{Upload chart image?}
+    Upload -->|Yes| Validate{Image valid?}
+    Validate -->|No| Error[Show error message]
+    Validate -->|Yes| Question{Enter question?}
+    Question -->|No| Auto[Use default analysis]
+    Question -->|Yes| Model{Select model?}
+    Auto --> Model
+    
+    Model -->|ChartGemma| CG[Process with ChartGemma]
+    Model -->|Ollama| OL[Process with Ollama]
+    Model -->|Default| Default[Use default model]
+    
+    CG --> Advanced{Advanced analysis?}
+    Advanced -->|Yes| Enhance[Add Program of Thought]
+    Advanced -->|No| Process[Process normally]
+    Enhance --> Process
+    OL --> Process
+    Default --> Process
+    
+    Process --> Result[Generate analysis result]
+    Result --> Save[Save to database]
+    Save --> Display[Display result to user]
+    Display --> End([Analysis complete])
+    
+    Error --> End
+```
+
+## 6. State Diagram - User Authentication
+
+```mermaid
+stateDiagram-v2
+    [*] --> Unauthenticated
+    Unauthenticated --> Registering : Register
+    Unauthenticated --> LoggingIn : Login
+    Registering --> Unauthenticated : Registration failed
+    Registering --> Authenticated : Registration success
+    LoggingIn --> Unauthenticated : Login failed
+    LoggingIn --> Authenticated : Login success
+    Authenticated --> Unauthenticated : Logout
+    Authenticated --> TokenExpired : Token expires
+    TokenExpired --> Unauthenticated : Auto logout
+    TokenExpired --> Authenticated : Refresh token
+```
+
+## 7. Deployment Diagram
+
+```mermaid
+graph TB
+    subgraph "Client Layer"
+        Browser[Web Browser]
+        Mobile[Mobile App]
+    end
+    
+    subgraph "Frontend Server"
+        React[React App<br/>Vite Dev Server]
+        Static[Static Assets]
+    end
+    
+    subgraph "Backend Server"
+        FastAPI[FastAPI Application]
+        Uvicorn[Uvicorn Server]
+    end
+    
+    subgraph "Database Server"
+        PostgreSQL[(PostgreSQL Database)]
+        Alembic[Alembic Migrations]
+    end
+    
+    subgraph "External Services"
+        ChartGemmaAPI[ChartGemma API<br/>Gradio]
+        OllamaAPI[Ollama API<br/>Local]
+    end
+    
+    %% Connections
+    Browser --> React
+    Mobile --> React
+    React --> Static
+    React --> FastAPI
+    FastAPI --> Uvicorn
+    Uvicorn --> PostgreSQL
+    Uvicorn --> Alembic
+    FastAPI --> ChartGemmaAPI
+    FastAPI --> OllamaAPI
+```
+
+## 8. Data Flow Diagram
+
+```mermaid
+graph LR
+    subgraph "Input Sources"
+        UserInput[User Input]
+        ImageUpload[Image Upload]
+        QuestionInput[Question Input]
+    end
+    
+    subgraph "Processing"
+        Auth[Authentication]
+        ImageProc[Image Processing]
+        LLMProc[LLM Processing]
+        Analysis[Analysis Engine]
+    end
+    
+    subgraph "Storage"
+        UserDB[(User Database)]
+        ImageDB[(Image Storage)]
+        AnalysisDB[(Analysis History)]
+    end
+    
+    subgraph "Output"
+        Response[API Response]
+        UI[User Interface]
+        Logs[System Logs]
+    end
+    
+    %% Data flows
+    UserInput --> Auth
+    ImageUpload --> ImageProc
+    QuestionInput --> LLMProc
+    
+    Auth --> UserDB
+    ImageProc --> ImageDB
+    LLMProc --> Analysis
+    Analysis --> AnalysisDB
+    
+    Analysis --> Response
+    Response --> UI
+    Auth --> Logs
+    Analysis --> Logs
+``` 
\ No newline at end of file

main.py

 from fastapi import FastAPI
 from src.infrastructure.api.fastapi.routes import auth, charts
 from fastapi.middleware.cors import CORSMiddleware
-
 from sqlalchemy import create_engine
 from sqlalchemy.exc import OperationalError
+from src.config import settings
 
-app = FastAPI(debug = "True",
+app = FastAPI(
+    debug=True,
     title="Chart Analyzer API",
     description="API for analyzing charts and managing users.",
     version="1.0.0",
@@ -13,11 +14,11 @@ app = FastAPI(debug = "True",
 
 @app.on_event("startup")
 async def startup_event():
-    engine = create_engine("postgresql+psycopg://chart_analyzer_user:chartanalyzer13@localhost:5432/chart_analyzer")
+    engine = create_engine(settings.DATABASE_URL)
     try:
         with engine.connect() as conn:
             print("✅ Database connection successful")
-            print(f"🔗 Connection string: postgresql+psycopg://chart_analyzer_user:chartanalyzer13@localhost:5432/chart_analyzer")
+            print(f"🔗 Connection string: {settings.DATABASE_URL}")
     except OperationalError as e:
         print("❌ Database connection failed")
         print(f"Error: {e}")
@@ -31,10 +32,17 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-
+# Include routers
 app.include_router(auth.router, prefix="/auth")
 app.include_router(charts.router, prefix="/charts")
 
 @app.get("/")
 async def root():
-    return {"message": "Welcome to Chart Analyzer"}
+    return {
+        "message": "Welcome to Chart Analyzer API",
+        "version": "1.0.0",
+        "endpoints": {
+            "authentication": "/auth",
+            "chart_analysis": "/charts"
+        }
+    }

src/application/dtos/LLM.py

@@ -11,7 +11,6 @@ class LLMRequestDTO(BaseModel):
     """Input for LLM analysis requests"""
     image_bytes: bytes = Field(..., description="Binary image data (PNG/JPEG)")
     question: str = Field(..., max_length=500, description="Question about the chart")
-    provider: LLMProvider = Field(LLMProvider.HUGGINGFACE, description="LLM service provider")
     max_tokens: int = Field(300, gt=0, le=2000, description="Max response length")
     # temperature: float = Field(0.3, ge=0.1, le=1.0, description="Creativity control")
 

src/application/dtos/conversation.py

+from pydantic import BaseModel
+from typing import List, Optional
+
+class CreateConversationRequestDTO(BaseModel):
+    chart_image_id: str
+    title: str
+
+class SendMessageRequestDTO(BaseModel):
+    conversation_id: str
+    message: str
+
+class ConversationResponseDTO(BaseModel):
+    id: str
+    title: str
+    created_at: str
+    updated_at: str
+    message_count: int
+
+class MessageResponseDTO(BaseModel):
+    id: str
+    message_type: str
+    content: str
+    timestamp: str
+
+class ConversationDetailResponseDTO(BaseModel):
+    id: str
+    title: str
+    created_at: str
+    updated_at: str
+    messages: List[MessageResponseDTO] 
\ No newline at end of file

src/application/services/chartGemma_service.py

+import httpx
+from src.application.ports.llm_service_port import LLMServicePort
+from src.application.dtos.LLM import LLMRequestDTO, LLMResponseDTO
+from typing import Optional
+import base64
+import io
+from PIL import Image
+import json
+import tempfile
+import os
+
+class ChartGemmaService(LLMServicePort):
+    def __init__(self, gradio_url: str):
+        self.gradio_url = gradio_url
+    
+    async def analyze(self, request: LLMRequestDTO) -> LLMResponseDTO:
+        try:
+            # Check if image data is provided
+            if not request.image_bytes:
+                return LLMResponseDTO(
+                    answer="Error: No image data provided",
+                    model_used="ChartGemma",
+                    processing_time=None
+                )
+            
+            # Convert image (if provided as bytes) to PIL Image
+            image = self._load_image(request.image_bytes)
+            
+            if not image:
+                return LLMResponseDTO(
+                    answer="Error: Could not load image data",
+                    model_used="ChartGemma",
+                    processing_time=None
+                )
+            
+            # Call Gradio API
+            response = await self._call_gradio_api(
+                image=image,
+                question=request.question
+            )
+            
+            return LLMResponseDTO(
+                answer=response,
+                model_used="ChartGemma",
+                processing_time=None  # Gradio doesn't return this
+            )
+        except Exception as e:
+            # Return a more informative error response
+            return LLMResponseDTO(
+                answer=f"Error analyzing chart: {str(e)}",
+                model_used="ChartGemma",
+                processing_time=None
+            )
+    
+    def _load_image(self, image_data: Optional[bytes]) -> Optional[Image.Image]:
+        if not image_data:
+            return None
+        try:
+            return Image.open(io.BytesIO(image_data)).convert("RGB")
+        except Exception as e:
+            print(f"Error loading image: {e}")
+            return None
+    
+    async def _call_gradio_api(self, image: Image.Image, question: str) -> str:
+        # Save image to a temporary file (since Gradio expects filepath)
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_file:
+            image.save(tmp_file, format='PNG')
+            tmp_file_path = tmp_file.name
+
+        try:
+            # Prepare the payload according to Gradio's API format
+            payload = {
+                "data": [
+                    tmp_file_path,  # Filepath to the image
+                    question       # Optional question
+                ]
+            }
+
+            async with httpx.AsyncClient() as client:
+                # The Gradio API endpoint is /api/predict/
+                api_url = f"{self.gradio_url.rstrip('/')}/run/predict/"
+                response = await client.post(api_url, json=payload)
+                response.raise_for_status()
+
+                # Gradio returns the output in the "data" field as a list
+                return response.json()["data"][0]
+        finally:
+            # Clean up the temporary file
+            try:
+                os.unlink(tmp_file_path)
+            except:
+                pass
\ No newline at end of file

src/application/use_cases/chat_conversation.py

+from src.domain.entities.chart_analysis import ChartAnalysis
+from src.domain.ports.repositories.analysis_repository import AnalysisRepositoryPort
+from src.application.ports.llm_service_port import LLMServicePort
+from src.application.dtos.LLM import LLMRequestDTO
+import uuid
+from datetime import datetime, timezone
+from typing import List
+
+class ChatConversationUseCase:
+    def __init__(
+        self, 
+        analysis_repo: AnalysisRepositoryPort,
+        llm_service: LLMServicePort
+    ):
+        self._analysis_repo = analysis_repo
+        self._llm_service = llm_service
+
+    def execute(self, chart_image_id: str, conversation_history: List[str], new_question: str) -> ChartAnalysis:
+        """Handle interactive chat conversation about a chart"""
+        
+        # Build context from conversation history
+        context = self._build_conversation_context(conversation_history)
+        
+        # Create enhanced question with context
+        enhanced_question = f"Context from previous conversation: {context}\n\nNew question: {new_question}"
+        
+        # Create request for LLM
+        request = LLMRequestDTO(
+            image_bytes=b"",  # Will be loaded by service
+            question=enhanced_question
+        )
+        
+        # Get response from LLM
+        response = await self._llm_service.analyze(request)
+        
+        # Save the conversation turn
+        analysis = ChartAnalysis(
+            id=str(uuid.uuid4()),
+            chart_image_id=chart_image_id,
+            question=new_question,
+            answer=response.answer,
+            created_at=datetime.now(timezone.utc)
+        )
+        
+        self._analysis_repo.save_analysis(analysis)
+        return analysis
+    
+    def _build_conversation_context(self, history: List[str]) -> str:
+        """Build context string from conversation history"""
+        if not history:
+            return ""
+        
+        context_parts = []
+        for i, entry in enumerate(history[-5:], 1):  # Last 5 entries for context
+            context_parts.append(f"Turn {i}: {entry}")
+        
+        return " | ".join(context_parts) 
\ No newline at end of file

src/application/use_cases/get_analysis_history.py

+from src.domain.ports.repositories.analysis_repository import AnalysisRepositoryPort
+from src.domain.entities.chart_analysis import ChartAnalysis
+from typing import List
+
+class GetAnalysisHistoryUseCase:
+    def __init__(self, analysis_repo: AnalysisRepositoryPort):
+        self._analysis_repo = analysis_repo
+
+    def execute(self, user_id: str, limit: int = 50) -> List[ChartAnalysis]:
+        """Get analysis history for a user"""
+        return self._analysis_repo.get_analyses_by_user_id(user_id, limit) 
\ No newline at end of file

src/application/use_cases/save_analysis.py

+from src.domain.entities.chart_analysis import ChartAnalysis
+from src.domain.ports.repositories.analysis_repository import AnalysisRepositoryPort
+from src.application.dtos.LLM import LLMResponseDTO
+import uuid
+from datetime import datetime, timezone
+
+class SaveAnalysisUseCase:
+    def __init__(self, analysis_repo: AnalysisRepositoryPort):
+        self._analysis_repo = analysis_repo
+
+    def execute(self, chart_image_id: str, question: str, llm_response: LLMResponseDTO) -> ChartAnalysis:
+        """Save analysis result to database for history tracking"""
+        analysis = ChartAnalysis(
+            id=str(uuid.uuid4()),
+            chart_image_id=chart_image_id,
+            question=question,
+            answer=llm_response.answer,
+            created_at=datetime.now(timezone.utc)
+        )
+        
+        self._analysis_repo.save_analysis(analysis)
+        return analysis 
\ No newline at end of file

src/config.py

@@ -8,6 +8,12 @@ class Settings(BaseSettings):
     JWT_SECRET: str = "zKReJQaoTK_F9Y2EIkDKZS1hgnfOZsplgzbjXY7IWyc"
     JWT_ALGORITHM: str = "HS256"
     JWT_EXPIRE_MINUTES: int = 30
+    
+    # LLM Service Configuration
+    OLLAMA_HOST: str = "http://localhost:11434"
+    OLLAMA_MODEL: str = "llava:34b"
+    CHARTGEMMA_GRADIO_URL: str = "https://3f4f53fba3f99b8778.gradio.live/"
+    DEFAULT_LLM_MODEL: str = "ollama"
 
     # Use model_config instead of inner Config class
     model_config = ConfigDict(

src/dependencies.py

-from fastapi import Depends
+from fastapi import Depends, Query
 from typing import AsyncGenerator
 from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
 from sqlalchemy.orm import sessionmaker
@@ -10,30 +10,15 @@ from src.domain.ports.repositories.charts_repository import ChartsRepositoryPort
 from src.infrastructure.adapters.sqlserver.sql_user_repository import SqlUserRepository
 from src.infrastructure.adapters.sqlserver.sql_charts_repository import SqlChartsRepository
 from src.application.services.analyze_service import AnalyzeService
-from src.application.services.llm_service import LLMService
+from src.application.services.ollama_service import OllamaService
+from src.application.services.chartGemma_service import ChartGemmaService
 from src.application.ports.llm_service_port import LLMServicePort
 from src.infrastructure.adapters.sqlserver.sql_token_repository import SqlTokenRepository
-# from infrastructure.services.llm.openai_service import OpenAIService  # Concrete LLM impl
-# from infrastructure.services.image.pillow_service import PillowImageService  # Concrete impl
 from src.config import settings
-from src.application.services.ollama_service import OllamaService
-
 
 engine = create_async_engine(settings.DATABASE_URL, echo=True)
 AsyncSessionLocal = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
 
-
-
-
-
-
-
-
-
-def get_llm_service() -> LLMServicePort:
-    return OllamaService(host="http://172.25.1.141:11434")
-
-
 async def get_db_session() -> AsyncGenerator[AsyncSession, None]:
     async with AsyncSessionLocal() as session:
         yield session
@@ -44,10 +29,9 @@ def get_user_repository(session: AsyncSession = Depends(get_db_session)) -> User
 def get_charts_repository(session: AsyncSession = Depends(get_db_session)) -> ChartsRepositoryPort:
     return SqlChartsRepository(session)
 
-def get_token_repo(session: AsyncSession = Depends(get_db_session)) -> SqlTokenRepository:
+def get_token_repo(session: AsyncSession = Depends(get_db_session)) -> TokenRepositoryPort:
     return SqlTokenRepository(session)
 
-
 def get_auth_service(
     user_repo: UserRepositoryPort = Depends(get_user_repository),
     token_repo: TokenRepositoryPort = Depends(get_token_repo)
@@ -57,11 +41,38 @@ def get_auth_service(
 def get_upload_use_case(charts_repo: ChartsRepositoryPort = Depends(get_charts_repository)) -> UploadChartUseCase:
     return UploadChartUseCase(charts_repo)
 
-
-
 def get_analysis_service() -> AnalyzeService:
     """Factory for the analysis service"""
-    llm_service = LLMService()
-    # image_service = PillowImageService()
+    llm_service = get_llm_service()
     return AnalyzeService(llm_service)
 
+def get_ollama_service() -> OllamaService:
+    """Factory for Ollama LLM service"""
+    return OllamaService(host=settings.OLLAMA_HOST)
+
+def get_chartgemma_service() -> ChartGemmaService:
+    """Factory for ChartGemma LLM service"""
+    return ChartGemmaService(gradio_url=settings.CHARTGEMMA_GRADIO_URL)
+
+def get_llm_service(model: str = settings.DEFAULT_LLM_MODEL) -> LLMServicePort:
+    """
+    Factory function to get the appropriate LLM service based on model selection.
+    This allows easy switching between different LLM providers.
+    """
+    if model.lower() == "ollama":
+        return get_ollama_service()
+    elif model.lower() == "chartgemma":
+        return get_chartgemma_service()
+    else:
+        # Default to Ollama if unknown model is specified
+        return get_ollama_service()
+
+def get_llm_service_by_query(
+    model: str = Query(default=settings.DEFAULT_LLM_MODEL, description="LLM model to use (ollama or chartgemma)")
+) -> LLMServicePort:
+    """
+    Dependency function that allows model selection via query parameter.
+    This provides a clean way for users to select their preferred LLM model.
+    """
+    return get_llm_service(model)
+

src/domain/entities/conversation.py

+from pydantic import BaseModel
+from datetime import datetime, timezone
+from typing import List, Optional
+
+class ConversationMessage(BaseModel):
+    id: str
+    conversation_id: str
+    user_id: str
+    message_type: str  # "user" or "assistant"
+    content: str
+    timestamp: datetime = datetime.now(timezone.utc)
+
+class Conversation(BaseModel):
+    id: str
+    user_id: str
+    chart_image_id: str
+    title: str
+    created_at: datetime = datetime.now(timezone.utc)
+    updated_at: datetime = datetime.now(timezone.utc)
+    is_active: bool = True
+    messages: List[ConversationMessage] = [] 
\ No newline at end of file

src/infrastructure/api/fastapi/routes/charts.py

-from fastapi import APIRouter, Form, UploadFile, File, Depends
-from src.application.services.analyze_service import AnalyzeService
-from src.dependencies import get_upload_use_case
-from src.application.dtos.LLM import LLMResponseDTO, LLMRequestDTO
+from fastapi import APIRouter, UploadFile, File, HTTPException, Depends, Query
 from src.application.ports.llm_service_port import LLMServicePort
-from src.dependencies import get_llm_service
+from src.application.dtos.LLM import LLMRequestDTO
+from src.dependencies import get_llm_service_by_query, get_llm_service
+from src.config import settings
+from typing import Optional
+import logging
 
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=["charts"])
 
-
-@router.post("/ask", response_model=LLMResponseDTO)
-async def ask_about_chart(
+@router.post("/analyze")
+async def analyze_general(
     image: UploadFile = File(...),
-    question: str = Form(...),
-    llm_service: LLMServicePort = Depends(get_llm_service)
+    question: str = Query(default="", description="Question about the chart"),
+    llm_service: LLMServicePort = Depends(get_llm_service_by_query)
 ):
-    request = LLMRequestDTO(
-        image_bytes=await image.read(),
-        question=question
-    )
-    return await llm_service.analyze(request)
-
-
+    """
+    Unified endpoint that works with both Ollama and ChartGemma models.
+    Users can select the model via the 'model' query parameter.
+    """
+    try:
+        logger.info(f"Starting analysis with question: {question}")
+        logger.info(f"Image filename: {image.filename}, content_type: {image.content_type}")
+        
+        image_bytes = await image.read()
+        logger.info(f"Image size: {len(image_bytes)} bytes")
+        
+        request = LLMRequestDTO(
+            image_bytes=image_bytes,
+            question=question
+        )
+        
+        logger.info("Calling LLM service...")
+        response = await llm_service.analyze(request)
+        logger.info(f"LLM service response received: {response.answer[:100]}...")
+        
+        return {
+            "answer": response.answer,
+            "model": response.model_used,
+            "processing_time": response.processing_time
+        }
+        
+    except Exception as e:
+        logger.error(f"Error in analyze_general: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
 
-@router.post("/analyze", response_model=LLMResponseDTO)
-async def analyze_chart(
+@router.post("/ask")
+async def analyze_chart_specialized(
     image: UploadFile = File(...),
-    # question: str = Form(...),
-    llm_service: LLMServicePort = Depends(get_llm_service)
+    question: str = Query(default="", description="Question about the chart"),
+    advanced_analysis: bool = Query(default=False, description="Enable advanced analysis with program of thought")
 ):
-    request = LLMRequestDTO(
-        image_bytes=await image.read(),
-        question="analyze the trends showing in this chart"
-    )
-    return await llm_service.analyze(request)
\ No newline at end of file
+    """
+    Specialized endpoint for chart analysis with ChartGemma.
+    - advanced_analysis: When True, adds specific prompt engineering for charts
+    """
+    try:
+        logger.info(f"Starting specialized analysis with question: {question}, advanced: {advanced_analysis}")
+        
+        # Force ChartGemma for specialized chart analysis
+        service = get_llm_service("chartgemma")
+        
+        # Enhanced prompt for charts if needed
+        processed_question = f"PROGRAM OF THOUGHT: {question}" if advanced_analysis else question
+        
+        image_bytes = await image.read()
+        request = LLMRequestDTO(
+            image_bytes=image_bytes,
+            question=processed_question
+        )
+        
+        logger.info("Calling ChartGemma service...")
+        response = await service.analyze(request)
+        logger.info(f"ChartGemma service response received: {response.answer[:100]}...")
+        
+        return {
+            "answer": response.answer,
+            "model": "ChartGemma",
+            "analysis_type": "advanced" if advanced_analysis else "basic",
+            "processing_time": response.processing_time
+        }
+        
+    except Exception as e:
+        logger.error(f"Error in analyze_chart_specialized: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
+
+@router.get("/test-chartgemma")
+async def test_chartgemma_connection():
+    """
+    Test endpoint to verify ChartGemma service connection and response structure.
+    """
+    try:
+        logger.info("Testing ChartGemma service connection...")
+        
+        # Create a simple test request without an image
+        service = get_llm_service("chartgemma")
+        
+        # Test the service with a minimal request
+        test_request = LLMRequestDTO(
+            image_bytes=b"",  # Empty image for testing
+            question="Test question"
+        )
+        
+        response = await service.analyze(test_request)
+        
+        return {
+            "status": "success",
+            "service": "ChartGemma",
+            "response": response.answer,
+            "model_used": response.model_used
+        }
+        
+    except Exception as e:
+        logger.error(f"Error testing ChartGemma: {str(e)}", exc_info=True)
+        return {
+            "status": "error",
+            "service": "ChartGemma",
+            "error": str(e)
+        }
+
+@router.get("/models")
+async def list_available_models():
+    """
+    Endpoint to list available LLM services and their configurations.
+    """
+    return {
+        "available_models": ["ollama", "chartgemma"],
+        "default_model": settings.DEFAULT_LLM_MODEL,
+        "model_configs": {
+            "ollama": {
+                "host": settings.OLLAMA_HOST,
+                "model": settings.OLLAMA_MODEL,
+                "description": "Local Ollama LLM service"
+            },
+            "chartgemma": {
+                "gradio_url": settings.CHARTGEMMA_GRADIO_URL,
+                "description": "Specialized chart analysis model"
+            }
+        }
+    }
\ No newline at end of file

src/infrastructure/api/fastapi/routes/conversations.py

+from fastapi import APIRouter, Depends, HTTPException, status
+from src.application.dtos.conversation import (
+    CreateConversationRequestDTO,
+    SendMessageRequestDTO,
+    ConversationResponseDTO,
+    ConversationDetailResponseDTO
+)
+from src.application.use_cases.chat_conversation import ChatConversationUseCase
+from src.dependencies import get_current_user
+from typing import List
+import logging
+
+logger = logging.getLogger(__name__)
+router = APIRouter(tags=["conversations"])
+
+@router.post("/", response_model=ConversationResponseDTO, status_code=status.HTTP_201_CREATED)
+async def create_conversation(
+    request: CreateConversationRequestDTO,
+    current_user: dict = Depends(get_current_user)
+):
+    """Create a new conversation for a chart"""
+    try:
+        # Implementation would use conversation service
+        # For now, return mock response
+        return ConversationResponseDTO(
+            id="conv_123",
+            title=request.title,
+            created_at="2024-01-01T00:00:00Z",
+            updated_at="2024-01-01T00:00:00Z",
+            message_count=0
+        )
+    except Exception as e:
+        logger.error(f"Error creating conversation: {str(e)}")
+        raise HTTPException(status_code=500, detail="Failed to create conversation")
+
+@router.get("/", response_model=List[ConversationResponseDTO])
+async def list_conversations(
+    current_user: dict = Depends(get_current_user),
+    limit: int = 20
+):
+    """List user's conversations"""
+    try:
+        # Implementation would fetch from repository
+        return []
+    except Exception as e:
+        logger.error(f"Error listing conversations: {str(e)}")
+        raise HTTPException(status_code=500, detail="Failed to list conversations")
+
+@router.get("/{conversation_id}", response_model=ConversationDetailResponseDTO)
+async def get_conversation(
+    conversation_id: str,
+    current_user: dict = Depends(get_current_user)
+):
+    """Get conversation details with messages"""
+    try:
+        # Implementation would fetch from repository
+        return ConversationDetailResponseDTO(
+            id=conversation_id,
+            title="Sample Conversation",
+            created_at="2024-01-01T00:00:00Z",
+            updated_at="2024-01-01T00:00:00Z",
+            messages=[]
+        )
+    except Exception as e:
+        logger.error(f"Error getting conversation: {str(e)}")
+        raise HTTPException(status_code=500, detail="Failed to get conversation")
+
+@router.post("/{conversation_id}/messages", response_model=MessageResponseDTO)
+async def send_message(
+    conversation_id: str,
+    request: SendMessageRequestDTO,
+    current_user: dict = Depends(get_current_user)
+):
+    """Send a message in a conversation"""
+    try:
+        # Implementation would use ChatConversationUseCase
+        return MessageResponseDTO(
+            id="msg_123",
+            message_type="assistant",
+            content="This is a sample response from the AI.",
+            timestamp="2024-01-01T00:00:00Z"
+        )
+    except Exception as e:
+        logger.error(f"Error sending message: {str(e)}")
+        raise HTTPException(status_code=500, detail="Failed to send message") 
\ No newline at end of file