Update api/utils.py

api/utils.py

@@ -1,3 +1,5 @@
+# api/utils.py
+
 from datetime import datetime
 import json
 import uuid
@@ -9,16 +11,17 @@ from typing import Any, Dict, Optional, AsyncGenerator
 import httpx
 from fastapi import HTTPException
 from api.config import (
-    models,
-    model_aliases,
-    ALLOWED_MODELS,
-    MODEL_MAPPING,
+    MODELS,
+    MODEL_ALIASES,
+    DEFAULT_MODEL,
+    API_ENDPOINT,
     get_headers_api_chat,
     BASE_URL,
+    MODEL_PREFIXES,
+    MODEL_REFERERS
 )
 from api.models import ChatRequest, Message
 from api.logger import setup_logger
-from api.providers.gizai import GizAI  # Import the GizAI provider
 
 logger = setup_logger(__name__)
 
@@ -27,7 +30,7 @@ def generate_chat_id(length: int = 7) -> str:
     characters = string.ascii_letters + string.digits
     return ''.join(random.choices(characters, k=length))
 
-# Helper function to create chat completion data
+# Helper function to create a chat completion data chunk
 def create_chat_completion_data(
     content: str, model: str, timestamp: int, finish_reason: Optional[str] = None
 ) -> Dict[str, Any]:
@@ -46,15 +49,11 @@ def create_chat_completion_data(
         "usage": None,
     }
 
-# Function to convert message to dictionary format, ensuring base64 data
-def message_to_dict(message: Message):
-    if isinstance(message.content, str):
-        content = message.content
-    elif isinstance(message.content, list) and isinstance(message.content[0], dict) and "text" in message.content[0]:
-        content = message.content[0]["text"]
-    else:
-        content = ""
-
+# Function to convert message to dictionary format, ensuring base64 data and optional model prefix
+def message_to_dict(message: Message, model_prefix: Optional[str] = None):
+    content = message.content if isinstance(message.content, str) else message.content[0]["text"]
+    if model_prefix:
+        content = f"{model_prefix} {content}"
     if isinstance(message.content, list) and len(message.content) == 2 and "image_url" in message.content[1]:
         # Ensure base64 images are always included for all models
         return {
@@ -68,91 +67,192 @@ def message_to_dict(message: Message):
         }
     return {"role": message.role, "content": content}
 
-# Function to resolve model aliases
-def resolve_model(model: str) -> str:
-    if model in MODEL_MAPPING:
-        return model
-    elif model in model_aliases:
-        return model_aliases[model]
-    else:
-        logger.warning(f"Model '{model}' not recognized. Using default model '{GizAI.default_model}'.")
-        return GizAI.default_model  # default_model
-
-# Process streaming response with GizAI provider
+# Function to strip model prefix from content if present
+def strip_model_prefix(content: str, model_prefix: Optional[str] = None) -> str:
+    """Remove the model prefix from the response content if present."""
+    if model_prefix and content.startswith(model_prefix):
+        logger.debug(f"Stripping prefix '{model_prefix}' from content.")
+        return content[len(model_prefix):].strip()
+    return content
+
+# Function to get the correct referer URL for logging
+def get_referer_url(chat_id: str, model: str) -> str:
+    """Generate the referer URL based on specific models listed in MODEL_REFERERS."""
+    if model in MODEL_REFERERS:
+        return f"{BASE_URL}/chat/{chat_id}?model={model}"
+    return BASE_URL
+
+# Helper function to format messages
+def format_messages(messages: list[Message]) -> str:
+    # Assuming messages need to be concatenated in some way
+    return "\n".join([msg.content if isinstance(msg.content, str) else msg.content[0]["text"] for msg in messages])
+
+# Process streaming response
 async def process_streaming_response(request: ChatRequest) -> AsyncGenerator[str, None]:
     chat_id = generate_chat_id()
-    resolved_model = resolve_model(request.model)
-    logger.info(f"Generated Chat ID: {chat_id} - Model: {resolved_model}")
-
-    # Instantiate the GizAI provider
-    gizai_provider = GizAI()
-
-    # Create the async generator
-    async for response in gizai_provider.create_async_generator(
-        model=resolved_model,
-        messages=request.messages,
-        proxy=request.proxy  # Assuming 'proxy' is part of ChatRequest; if not, adjust accordingly
-    ):
-        timestamp = int(datetime.now().timestamp())
-        if isinstance(response, ImageResponse):
-            # Handle image responses
-            yield f"data: {json.dumps({'image_url': response.images, 'alt': response.alt})}\n\n"
-        else:
-            # Handle text responses
-            yield f"data: {json.dumps(create_chat_completion_data(response, resolved_model, timestamp))}\n\n"
-
-    # Indicate completion
-    timestamp = int(datetime.now().timestamp())
-    yield f"data: {json.dumps(create_chat_completion_data('', resolved_model, timestamp, 'stop'))}\n\n"
-    yield "data: [DONE]\n\n"
-
-# Process non-streaming response with GizAI provider
+    referer_url = get_referer_url(chat_id, request.model)
+    logger.info(f"Generated Chat ID: {chat_id} - Model: {request.model} - URL: {referer_url}")
+
+    model = request.model if request.model in MODELS else MODEL_ALIASES.get(request.model, DEFAULT_MODEL)
+    model_prefix = MODEL_PREFIXES.get(model, "")
+
+    headers_api_chat = get_headers_api_chat(referer_url)
+
+    # Prepare data based on model type
+    if model in ['flux1', 'sdxl', 'sd', 'sd35']:  # Image models
+        prompt = request.messages[-1].content if isinstance(request.messages[-1].content, str) else request.messages[-1].content[0]["text"]
+        data = {
+            "model": model,
+            "input": {
+                "width": "1024",
+                "height": "1024",
+                "steps": 4,
+                "output_format": "webp",
+                "batch_size": 1,
+                "mode": "plan",
+                "prompt": prompt
+            }
+        }
+    else:  # Chat models
+        data = {
+            "model": model,
+            "input": {
+                "messages": [
+                    {
+                        "type": "human",
+                        "content": f"{model_prefix} {format_messages(request.messages)}" if model_prefix else format_messages(request.messages)
+                    }
+                ],
+                "mode": "plan"
+            },
+            "noStream": False  # Assuming streaming
+        }
+
+    async with httpx.AsyncClient() as client:
+        try:
+            async with client.post(
+                API_ENDPOINT,
+                headers=headers_api_chat,
+                json=data,
+                timeout=100
+            ) as response:
+                response.raise_for_status()
+                # Assuming the API returns a streaming response
+                async for line in response.aiter_lines():
+                    timestamp = int(datetime.now().timestamp())
+                    if line:
+                        content = line
+                        # Depending on GizAI's response format, adjust parsing
+                        # Placeholder for content processing
+                        # Assuming content contains the message
+                        cleaned_content = strip_model_prefix(content, model_prefix)
+                        yield f"data: {json.dumps(create_chat_completion_data(cleaned_content, model, timestamp))}\n\n"
+
+                # Indicate end of stream
+                yield f"data: {json.dumps(create_chat_completion_data('', model, int(datetime.now().timestamp()), 'stop'))}\n\n"
+                yield "data: [DONE]\n\n"
+        except httpx.HTTPStatusError as e:
+            logger.error(f"HTTP error occurred for Chat ID {chat_id}: {e}")
+            raise HTTPException(status_code=e.response.status_code, detail=str(e))
+        except httpx.RequestError as e:
+            logger.error(f"Error occurred during request for Chat ID {chat_id}: {e}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+# Process non-streaming response
 async def process_non_streaming_response(request: ChatRequest) -> Dict[str, Any]:
     chat_id = generate_chat_id()
-    resolved_model = resolve_model(request.model)
-    logger.info(f"Generated Chat ID: {chat_id} - Model: {resolved_model}")
-
-    # Instantiate the GizAI provider
-    gizai_provider = GizAI()
-
-    # Collect the responses
-    responses = []
-    async for response in gizai_provider.create_async_generator(
-        model=resolved_model,
-        messages=request.messages,
-        proxy=request.proxy  # Assuming 'proxy' is part of ChatRequest; if not, adjust accordingly
-    ):
-        if isinstance(response, ImageResponse):
-            # For image responses, collect image URLs
-            responses.append({"image_url": response.images, "alt": response.alt})
-        else:
-            # For text responses, append the text
-            responses.append(response)
+    referer_url = get_referer_url(chat_id, request.model)
+    logger.info(f"Generated Chat ID: {chat_id} - Model: {request.model} - URL: {referer_url}")
 
-    return {
-        "id": f"chatcmpl-{uuid.uuid4()}",
-        "object": "chat.completion",
-        "created": int(datetime.now().timestamp()),
-        "model": resolved_model,
-        "choices": [
-            {
-                "index": 0,
-                "message": {"role": "assistant", "content": responses},
-                "finish_reason": "stop",
+    model = request.model if request.model in MODELS else MODEL_ALIASES.get(request.model, DEFAULT_MODEL)
+    model_prefix = MODEL_PREFIXES.get(model, "")
+
+    headers_api_chat = get_headers_api_chat(referer_url)
+
+    # Prepare data based on model type
+    if model in ['flux1', 'sdxl', 'sd', 'sd35']:  # Image models
+        prompt = request.messages[-1].content if isinstance(request.messages[-1].content, str) else request.messages[-1].content[0]["text"]
+        data = {
+            "model": model,
+            "input": {
+                "width": "1024",
+                "height": "1024",
+                "steps": 4,
+                "output_format": "webp",
+                "batch_size": 1,
+                "mode": "plan",
+                "prompt": prompt
             }
-        ],
-        "usage": None,
-    }
+        }
+    else:  # Chat models
+        data = {
+            "model": model,
+            "input": {
+                "messages": [
+                    {
+                        "type": "human",
+                        "content": f"{model_prefix} {format_messages(request.messages)}" if model_prefix else format_messages(request.messages)
+                    }
+                ],
+                "mode": "plan"
+            },
+            "noStream": True  # Non-streaming
+        }
+
+    async with httpx.AsyncClient() as client:
+        try:
+            response = await client.post(
+                API_ENDPOINT,
+                headers=headers_api_chat,
+                json=data,
+                timeout=100
+            )
+            response.raise_for_status()
+            response_data = response.json()
 
-# Helper function to format prompt from messages
-def format_prompt(messages: list[Message]) -> str:
-    # Implement the prompt formatting as per GizAI's requirements
-    # Placeholder implementation
-    formatted_messages = []
-    for msg in messages:
-        if isinstance(msg.content, str):
-            formatted_messages.append(msg.content)
-        elif isinstance(msg.content, list):
-            text = msg.content[0].get("text", "")
-            formatted_messages.append(text)
-    return "\n".join(formatted_messages)
+            # Process response based on GizAI's API response structure
+            # Placeholder: assuming 'output' contains the generated content
+            if model in ['flux1', 'sdxl', 'sd', 'sd35']:  # Image models
+                if response_data.get('status') == 'completed' and response_data.get('output'):
+                    images = response_data['output']
+                    # Assuming images is a list of URLs
+                    # For non-streaming, return all images at once
+                    # Adjust according to actual response
+                    return {
+                        "id": f"chatcmpl-{uuid.uuid4()}",
+                        "object": "chat.completion",
+                        "created": int(datetime.now().timestamp()),
+                        "model": model,
+                        "choices": [
+                            {
+                                "index": 0,
+                                "message": {"role": "assistant", "content": "", "images": images},
+                                "finish_reason": "stop",
+                            }
+                        ],
+                        "usage": None,
+                    }
+            else:  # Chat models
+                # Assuming response_data contains the full response
+                content = response_data.get('output', '')
+                cleaned_content = strip_model_prefix(content, model_prefix)
+                return {
+                    "id": f"chatcmpl-{uuid.uuid4()}",
+                    "object": "chat.completion",
+                    "created": int(datetime.now().timestamp()),
+                    "model": model,
+                    "choices": [
+                        {
+                            "index": 0,
+                            "message": {"role": "assistant", "content": cleaned_content},
+                            "finish_reason": "stop",
+                        }
+                    ],
+                    "usage": None,
+                }
+        except httpx.HTTPStatusError as e:
+            logger.error(f"HTTP error occurred for Chat ID {chat_id}: {e}")
+            raise HTTPException(status_code=e.response.status_code, detail=str(e))
+        except httpx.RequestError as e:
+            logger.error(f"Error occurred during request for Chat ID {chat_id}: {e}")
+            raise HTTPException(status_code=500, detail=str(e))