增加支持多模态，有图片识别功能和图片生成功能esperyong/gpt-cmd#2

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# Repo & Issue Metadata
Repo: gpt-cmd: No description provided.
Issue Title: 增加支持多模态，有图片识别功能和图片生成功能

Issue Description: ### Details
## 增加支持多模态，有图片识别功能和图片生成功能

目前只是使用了chatgpt的[Chat Completions API](https://platform.openai.com/docs/guides/text-generation/chat-completions-api)接口。所以无法支持vision功能。我希望能够增加这个功能，根据用户的问题，判断是否需要调用dall-e-3和gpt-4-vision-preview来接受vision的内容和生成图片的功能。



**Summaries of links found in the content:**

https://platform.openai.com/docs/guides/text-generation/chat-completions-api)接口。所以无法支持vision功能。我希望能够增加这个功能，根据用户的问题，判断是否需要调用dall-e-3和gpt-4-vision-preview来接受vision的内容和生成图片的功能:

The page metadata indicates that the page is not accessible due to JavaScript being turned off and cookies not being enabled. Therefore, it is not possible to provide a summary of the page content or any code snippets.

You have previously changed these files:
<changed_file file_path="gptcli/vision.py">
--- 
+++ 
@@ -0,0 +1,40 @@
+import base64
+import json
+
+import aiohttp
+
+
+class VisionAPIHandler:
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        self.headers = {"Authorization": f"Bearer {api_key}"}
+
+    async def generate_image(self, prompt: str, n_images: int = 1):
+        async with aiohttp.ClientSession() as session:
+            payload = {"prompt": prompt, "n": n_images}
+            async with session.post("https://api.openai.com/v1/images/generations", headers=self.headers, json=payload) as response:
+                if response.status == 200:
+                    return await response.json()
+                else:
+                    raise Exception(f"Failed to generate image: {response.status}")
+
+    async def recognize_image(self, image_path: str):
+        image_base64 = encode_image_to_base64(image_path)
+        async with aiohttp.ClientSession() as session:
+            payload = {"image": image_base64}
+            async with session.post("https://api.openai.com/v1/vision/recognitions", headers=self.headers, json=payload) as response:
+                if response.status == 200:
+                    return await response.json()
+                else:
+                    raise Exception(f"Failed to recognize image: {response.status}")
+
+def encode_image_to_base64(image_path: str) -> str:
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+def decode_base64_to_image(base64_str: str, output_path: str):
+    with open(output_path, "wb") as output_file:
+        output_file.write(base64.b64decode(base64_str))
+
+def decode_response_to_text(response: dict) -> str:
+    return response.get("data", {}).get("text", "")
</changed_file>
<changed_file file_path="gptcli/cli_vision_commands.py">
--- 
+++ 
@@ -0,0 +1,46 @@
+import argparse
+import sys
+
+from gptcli.vision import VisionAPIHandler
+
+
+def cli_generate_image():
+    parser = argparse.ArgumentParser(description="Generate images from prompts.")
+    parser.add_argument("prompt", type=str, help="Prompt for generating images.")
+    parser.add_argument("--n_images", type=int, default=1, help="Number of images to generate.")
+    args = parser.parse_args()
+
+    try:
+        vision_handler = VisionAPIHandler(api_key="Your_API_Key_Here")
+        response = vision_handler.generate_image(prompt=args.prompt, n_images=args.n_images)
+        for image_data in response["data"]:
+            print(f"Generated Image URL: {image_data['url']}")
+    except Exception as e:
+        print(f"Error generating image: {e}")
+        sys.exit(1)
+
+def cli_recognize_image():
+    parser = argparse.ArgumentParser(description="Recognize text from an image.")
+    parser.add_argument("image_path", type=str, help="Path to the image file.")
+    args = parser.parse_args()
+
+    try:
+        vision_handler = VisionAPIHandler(api_key="Your_API_Key_Here")
+        response = vision_handler.recognize_image(image_path=args.image_path)
+        print(f"Recognized Text: {response['data']['text']}")
+    except Exception as e:
+        print(f"Error recognizing image: {e}")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="CLI for image recognition and generation.")
+    parser.add_argument("--generate", action="store_true", help="Generate images from prompts.")
+    parser.add_argument("--recognize", action="store_true", help="Recognize text from an image.")
+    args = parser.parse_args()
+
+    if args.generate:
+        cli_generate_image()
+    elif args.recognize:
+        cli_recognize_image()
+    else:
+        parser.print_help()
</changed_file>

<section id="A">
import os
from typing import Dict, List, Optional, TypedDict
from attr import dataclass
import yaml

from gptcli.assistant import AssistantConfig
from gptcli.llama import LLaMAModelConfig


CONFIG_FILE_PATHS = [
    os.path.join(os.path.expanduser("~"), ".config", "gpt-cli", "gpt.yml"),
    os.path.join(os.path.expanduser("~"), ".gptrc"),
]


</section id="A">
<section id="B">
@dataclass
class GptCliConfig:
    default_assistant: str = "general"
    markdown: bool = True
    show_price: bool = True
    api_key: Optional[str] = os.environ.get("OPENAI_API_KEY")
    openai_api_key: Optional[str] = os.environ.get("OPENAI_API_KEY")
    anthropic_api_key: Optional[str] = os.environ.get("ANTHROPIC_API_KEY")
    google_api_key: Optional[str] = os.environ.get("GOOGLE_API_KEY")
    log_file: Optional[str] = None
    log_level: str = "INFO"
    conversations_save_directory: str = os.path.join(os.path.expanduser("~"), "Documents", "gpt-cli", "conversations")
    conversations_render_directory: str = os.path.join(os.path.expanduser("~"), "develop", "opencsg", "work", "docs", "opencsg", "gpt-generate-md")
    assistants: Dict[str, AssistantConfig] = {}
    interactive: Optional[bool] = None
    llama_models: Optional[Dict[str, LLaMAModelConfig]] = None


def choose_config_file(paths: List[str]) -> str:
    for path in paths:
        if os.path.isfile(path):
            return path
    return ""


def read_yaml_config(file_path: str) -> GptCliConfig:
    with open(file_path, "r") as file:
        config = yaml.safe_load(file)
        return GptCliConfig(
            **config,
        )
</section id="B">

# Request
• Add new configuration options for DALL-E 3 and GPT-4 vision API keys, `dalle_api_key` and `gpt4_vision_api_key`, respectively.
• Ensure these new keys are optional to maintain backward compatibility and default to `None` if not set.
• Update the `GptCliConfig` dataclass to include these new fields.

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -22,6 +22,8 @@
     openai_api_key: Optional[str] = os.environ.get("OPENAI_API_KEY")
     anthropic_api_key: Optional[str] = os.environ.get("ANTHROPIC_API_KEY")
     google_api_key: Optional[str] = os.environ.get("GOOGLE_API_KEY")
+    dalle_api_key: Optional[str] = os.environ.get("DALLE_API_KEY", None)
+    gpt4_vision_api_key: Optional[str] = os.environ.get("GPT4_VISION_API_KEY", None)
     log_file: Optional[str] = None
     log_level: str = "INFO"
     conversations_save_directory: str = os.path.join(os.path.expanduser("~"), "Documents", "gpt-cli", "conversations")

```
Here are the new code sections:




<section id="A">
import os
from typing import Dict, List, Optional, TypedDict
from attr import dataclass
import yaml

from gptcli.assistant import AssistantConfig
from gptcli.llama import LLaMAModelConfig


CONFIG_FILE_PATHS = [
    os.path.join(os.path.expanduser("~"), ".config", "gpt-cli", "gpt.yml"),
    os.path.join(os.path.expanduser("~"), ".gptrc"),
]


</section id="A">
<section id="B">
@dataclass
class GptCliConfig:
    default_assistant: str = "general"
    markdown: bool = True
    show_price: bool = True
    api_key: Optional[str] = os.environ.get("OPENAI_API_KEY")
    openai_api_key: Optional[str] = os.environ.get("OPENAI_API_KEY")
    anthropic_api_key: Optional[str] = os.environ.get("ANTHROPIC_API_KEY")
    google_api_key: Optional[str] = os.environ.get("GOOGLE_API_KEY")
    dalle_api_key: Optional[str] = os.environ.get("DALLE_API_KEY", None)
    gpt4_vision_api_key: Optional[str] = os.environ.get("GPT4_VISION_API_KEY", None)
    log_file: Optional[str] = None
    log_level: str = "INFO"
    conversations_save_directory: str = os.path.join(os.path.expanduser("~"), "Documents", "gpt-cli", "conversations")
</section id="B">
<section id="C">
    conversations_render_directory: str = os.path.join(os.path.expanduser("~"), "develop", "opencsg", "work", "docs", "opencsg", "gpt-generate-md")
    assistants: Dict[str, AssistantConfig] = {}
    interactive: Optional[bool] = None
    llama_models: Optional[Dict[str, LLaMAModelConfig]] = None


def choose_config_file(paths: List[str]) -> str:
    for path in paths:
        if os.path.isfile(path):
            return path
    return ""


def read_yaml_config(file_path: str) -> GptCliConfig:
    with open(file_path, "r") as file:
        config = yaml.safe_load(file)
        return GptCliConfig(
            **config,
        )
</section id="C">

You can continue to make changes to the code sections and call the `search_and_replace` function again.

# Repo & Issue Metadata
Repo: gpt-cmd: No description provided.
Issue Title: 增加支持多模态，有图片识别功能和图片生成功能

Issue Description: ### Details
## 增加支持多模态，有图片识别功能和图片生成功能

目前只是使用了chatgpt的[Chat Completions API](https://platform.openai.com/docs/guides/text-generation/chat-completions-api)接口。所以无法支持vision功能。我希望能够增加这个功能，根据用户的问题，判断是否需要调用dall-e-3和gpt-4-vision-preview来接受vision的内容和生成图片的功能。



**Summaries of links found in the content:**

https://platform.openai.com/docs/guides/text-generation/chat-completions-api)接口。所以无法支持vision功能。我希望能够增加这个功能，根据用户的问题，判断是否需要调用dall-e-3和gpt-4-vision-preview来接受vision的内容和生成图片的功能:

The page metadata indicates that the page is not accessible due to JavaScript being turned off and cookies not being enabled. Therefore, it is not possible to provide a summary of the page content or any code snippets.

You have previously changed these files:
<changed_file file_path="gptcli/vision.py">
--- 
+++ 
@@ -0,0 +1,40 @@
+import base64
+import json
+
+import aiohttp
+
+
+class VisionAPIHandler:
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        self.headers = {"Authorization": f"Bearer {api_key}"}
+
+    async def generate_image(self, prompt: str, n_images: int = 1):
+        async with aiohttp.ClientSession() as session:
+            payload = {"prompt": prompt, "n": n_images}
+            async with session.post("https://api.openai.com/v1/images/generations", headers=self.headers, json=payload) as response:
+                if response.status == 200:
+                    return await response.json()
+                else:
+                    raise Exception(f"Failed to generate image: {response.status}")
+
+    async def recognize_image(self, image_path: str):
+        image_base64 = encode_image_to_base64(image_path)
+        async with aiohttp.ClientSession() as session:
+            payload = {"image": image_base64}
+            async with session.post("https://api.openai.com/v1/vision/recognitions", headers=self.headers, json=payload) as response:
+                if response.status == 200:
+                    return await response.json()
+                else:
+                    raise Exception(f"Failed to recognize image: {response.status}")
+
+def encode_image_to_base64(image_path: str) -> str:
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+def decode_base64_to_image(base64_str: str, output_path: str):
+    with open(output_path, "wb") as output_file:
+        output_file.write(base64.b64decode(base64_str))
+
+def decode_response_to_text(response: dict) -> str:
+    return response.get("data", {}).get("text", "")
</changed_file>
<changed_file file_path="gptcli/cli_vision_commands.py">
--- 
+++ 
@@ -0,0 +1,46 @@
+import argparse
+import sys
+
+from gptcli.vision import VisionAPIHandler
+
+
+def cli_generate_image():
+    parser = argparse.ArgumentParser(description="Generate images from prompts.")
+    parser.add_argument("prompt", type=str, help="Prompt for generating images.")
+    parser.add_argument("--n_images", type=int, default=1, help="Number of images to generate.")
+    args = parser.parse_args()
+
+    try:
+        vision_handler = VisionAPIHandler(api_key="Your_API_Key_Here")
+        response = vision_handler.generate_image(prompt=args.prompt, n_images=args.n_images)
+        for image_data in response["data"]:
+            print(f"Generated Image URL: {image_data['url']}")
+    except Exception as e:
+        print(f"Error generating image: {e}")
+        sys.exit(1)
+
+def cli_recognize_image():
+    parser = argparse.ArgumentParser(description="Recognize text from an image.")
+    parser.add_argument("image_path", type=str, help="Path to the image file.")
+    args = parser.parse_args()
+
+    try:
+        vision_handler = VisionAPIHandler(api_key="Your_API_Key_Here")
+        response = vision_handler.recognize_image(image_path=args.image_path)
+        print(f"Recognized Text: {response['data']['text']}")
+    except Exception as e:
+        print(f"Error recognizing image: {e}")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="CLI for image recognition and generation.")
+    parser.add_argument("--generate", action="store_true", help="Generate images from prompts.")
+    parser.add_argument("--recognize", action="store_true", help="Recognize text from an image.")
+    args = parser.parse_args()
+
+    if args.generate:
+        cli_generate_image()
+    elif args.recognize:
+        cli_recognize_image()
+    else:
+        parser.print_help()
</changed_file>
<changed_file file_path="gptcli/config.py">
--- 
+++ 
@@ -43,4 +43,6 @@
         config = yaml.safe_load(file)
         return GptCliConfig(
             **config,
-        )+        )
+    dalle_api_key: Optional[str] = os.environ.get("DALLE_API_KEY")
+    gpt4_vision_api_key: Optional[str] = os.environ.get("GPT4_VISION_API_KEY")
</changed_file>

Relevant files in this PR:

<relevant_file file_path="gptcli/vision.py">
import base64
import json

import aiohttp


class VisionAPIHandler:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.headers = {"Authorization": f"Bearer {api_key}"}

    async def generate_image(self, prompt: str, n_images: int = 1):
        async with aiohttp.ClientSession() as session:
            payload = {"prompt": prompt, "n": n_images}
            async with session.post("https://api.openai.com/v1/images/generations", headers=self.headers, json=payload) as response:
                if response.status == 200:
                    return await response.json()
                else:
                    raise Exception(f"Failed to generate image: {response.status}")

    async def recognize_image(self, image_path: str):
        image_base64 = encode_image_to_base64(image_path)
        async with aiohttp.ClientSession() as session:
            payload = {"image": image_base64}
            async with session.post("https://api.openai.com/v1/vision/recognitions", headers=self.headers, json=payload) as response:
                if response.status == 200:
                    return await response.json()
                else:
                    raise Exception(f"Failed to recognize image: {response.status}")

def encode_image_to_base64(image_path: str) -> str:
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

def decode_base64_to_image(base64_str: str, output_path: str):
    with open(output_path, "wb") as output_file:
        output_file.write(base64.b64decode(base64_str))

def decode_response_to_text(response: dict) -> str:
    return response.get("data", {}).get("text", "")

</relevant_file>

<section id="A">
from typing import Iterator, List, cast
import openai
from openai import OpenAI
from openai.types.chat import ChatCompletionMessageParam

import tiktoken

from gptcli.completion import CompletionProvider, Message


class OpenAICompletionProvider(CompletionProvider):
</section id="A">
<section id="B">
    def __init__(self):
        self.client = OpenAI(api_key=openai.api_key)

    def complete(
        self, messages: List[Message], args: dict, stream: bool = False
    ) -> Iterator[str]:
        kwargs = {}
        if "temperature" in args:
            kwargs["temperature"] = args["temperature"]
        if "top_p" in args:
            kwargs["top_p"] = args["top_p"]

</section id="B">
<section id="C">
        if stream:
            response_iter = self.client.chat.completions.create(
                messages=cast(List[ChatCompletionMessageParam], messages),
                stream=True,
                model=args["model"],
                **kwargs,
            )

            for response in response_iter:
                next_choice = response.choices[0]
                if next_choice.finish_reason is None and next_choice.delta.content:
                    yield next_choice.delta.content
</section id="C">
<section id="D">
        else:
            response = self.client.chat.completions.create(
                messages=cast(List[ChatCompletionMessageParam], messages),
                model=args["model"],
                stream=False,
                **kwargs,
            )
            next_choice = response.choices[0]
            if next_choice.message.content:
</section id="D">
<section id="E">
                yield next_choice.message.content


def num_tokens_from_messages_openai(messages: List[Message], model: str) -> int:
    encoding = tiktoken.encoding_for_model(model)
    num_tokens = 0
    for message in messages:
        # every message follows <im_start>{role/name}\n{content}<im_end>\n
        num_tokens += 4
        for key, value in message.items():
            assert isinstance(value, str)
            num_tokens += len(encoding.encode(value))
            if key == "name":  # if there's a name, the role is omitted
                num_tokens += -1  # role is always required and always 1 token
    num_tokens += 2  # every reply is primed with <im_start>assistant
    return num_tokens


def num_tokens_from_completion_openai(completion: Message, model: str) -> int:
    return num_tokens_from_messages_openai([completion], model)
</section id="E">

# Request
• Integrate calls to `VisionAPIHandler` within the existing `OpenAICompletionProvider` class for cases where image processing is required.
• This integration will likely involve checking the type of input (text vs. image) and deciding whether to call the chat completion API or the vision API based on this input.
• Add logic to handle the responses from the vision API, converting them into a format suitable for the CLI output.

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -2,6 +2,8 @@
 import openai
 from openai import OpenAI
 from openai.types.chat import ChatCompletionMessageParam
+from gptcli.vision import VisionAPIHandler
+import asyncio
 
 import tiktoken
 

```
Here are the new code sections:




<section id="A">
from typing import Iterator, List, cast
import openai
from openai import OpenAI
from openai.types.chat import ChatCompletionMessageParam
from gptcli.vision import VisionAPIHandler
import asyncio

import tiktoken

from gptcli.completion import CompletionProvider, Message


</section id="A">
<section id="B">
class OpenAICompletionProvider(CompletionProvider):
    def __init__(self):
        self.client = OpenAI(api_key=openai.api_key)

    def complete(
        self, messages: List[Message], args: dict, stream: bool = False
    ) -> Iterator[str]:
        kwargs = {}
        if "temperature" in args:
            kwargs["temperature"] = args["temperature"]
        if "top_p" in args:
            kwargs["top_p"] = args["top_p"]

</section id="B">
<section id="C">
        if stream:
            response_iter = self.client.chat.completions.create(
                messages=cast(List[ChatCompletionMessageParam], messages),
                stream=True,
                model=args["model"],
                **kwargs,
            )

            for response in response_iter:
                next_choice = response.choices[0]
                if next_choice.finish_reason is None and next_choice.delta.content:
                    yield next_choice.delta.content
</section id="C">
<section id="D">
        else:
            response = self.client.chat.completions.create(
                messages=cast(List[ChatCompletionMessageParam], messages),
                model=args["model"],
                stream=False,
                **kwargs,
            )
            next_choice = response.choices[0]
            if next_choice.message.content:
</section id="D">
<section id="E">
                yield next_choice.message.content


def num_tokens_from_messages_openai(messages: List[Message], model: str) -> int:
    encoding = tiktoken.encoding_for_model(model)
    num_tokens = 0
    for message in messages:
        # every message follows <im_start>{role/name}\n{content}<im_end>\n
        num_tokens += 4
        for key, value in message.items():
            assert isinstance(value, str)
            num_tokens += len(encoding.encode(value))
            if key == "name":  # if there's a name, the role is omitted
                num_tokens += -1  # role is always required and always 1 token
    num_tokens += 2  # every reply is primed with <im_start>assistant
    return num_tokens


def num_tokens_from_completion_openai(completion: Message, model: str) -> int:
    return num_tokens_from_messages_openai([completion], model)
</section id="E">

You can continue to make changes to the code sections and call the `search_and_replace` function again.

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -13,6 +13,7 @@
 class OpenAICompletionProvider(CompletionProvider):
     def __init__(self):
         self.client = OpenAI(api_key=openai.api_key)
+        self.vision_handler = VisionAPIHandler(api_key=openai.api_key)
 
     def complete(
         self, messages: List[Message], args: dict, stream: bool = False

```
Here are the new code sections:




<section id="A">
from typing import Iterator, List, cast
import openai
from openai import OpenAI
from openai.types.chat import ChatCompletionMessageParam
from gptcli.vision import VisionAPIHandler
import asyncio

import tiktoken

from gptcli.completion import CompletionProvider, Message


</section id="A">
<section id="B">
class OpenAICompletionProvider(CompletionProvider):
    def __init__(self):
        self.client = OpenAI(api_key=openai.api_key)
        self.vision_handler = VisionAPIHandler(api_key=openai.api_key)

    def complete(
        self, messages: List[Message], args: dict, stream: bool = False
    ) -> Iterator[str]:
</section id="B">
<section id="C">
        kwargs = {}
        if "temperature" in args:
            kwargs["temperature"] = args["temperature"]
        if "top_p" in args:
            kwargs["top_p"] = args["top_p"]

        if stream:
            response_iter = self.client.chat.completions.create(
                messages=cast(List[ChatCompletionMessageParam], messages),
                stream=True,
                model=args["model"],
                **kwargs,
            )

            for response in response_iter:
                next_choice = response.choices[0]
                if next_choice.finish_reason is None and next_choice.delta.content:
                    yield next_choice.delta.content
</section id="C">
<section id="D">
        else:
            response = self.client.chat.completions.create(
                messages=cast(List[ChatCompletionMessageParam], messages),
                model=args["model"],
                stream=False,
                **kwargs,
            )
            next_choice = response.choices[0]
            if next_choice.message.content:
</section id="D">
<section id="E">
                yield next_choice.message.content


def num_tokens_from_messages_openai(messages: List[Message], model: str) -> int:
    encoding = tiktoken.encoding_for_model(model)
    num_tokens = 0
    for message in messages:
        # every message follows <im_start>{role/name}\n{content}<im_end>\n
        num_tokens += 4
        for key, value in message.items():
            assert isinstance(value, str)
            num_tokens += len(encoding.encode(value))
            if key == "name":  # if there's a name, the role is omitted
                num_tokens += -1  # role is always required and always 1 token
    num_tokens += 2  # every reply is primed with <im_start>assistant
    return num_tokens


def num_tokens_from_completion_openai(completion: Message, model: str) -> int:
    return num_tokens_from_messages_openai([completion], model)
</section id="E">

You can continue to make changes to the code sections and call the `search_and_replace` function again.

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -64,4 +64,13 @@
 
 
 def num_tokens_from_completion_openai(completion: Message, model: str) -> int:
+    return num_tokens_from_messages_openai([completion], model)
+
+    async def process_image_input(self, image_path: str) -> str:
+        try:
+            response = await self.vision_handler.recognize_image(image_path=image_path)
+            recognized_text = decode_response_to_text(response)
+            return recognized_text
+        except Exception as e:
+            return f'Error processing image: {e}'
     return num_tokens_from_messages_openai([completion], model)
```
Here are the new code sections:




<section id="A">
from typing import Iterator, List, cast
import openai
from openai import OpenAI
from openai.types.chat import ChatCompletionMessageParam
from gptcli.vision import VisionAPIHandler
import asyncio

import tiktoken

from gptcli.completion import CompletionProvider, Message


</section id="A">
<section id="B">
class OpenAICompletionProvider(CompletionProvider):
    def __init__(self):
        self.client = OpenAI(api_key=openai.api_key)
        self.vision_handler = VisionAPIHandler(api_key=openai.api_key)

    def complete(
        self, messages: List[Message], args: dict, stream: bool = False
    ) -> Iterator[str]:
</section id="B">
<section id="C">
        kwargs = {}
        if "temperature" in args:
            kwargs["temperature"] = args["temperature"]
        if "top_p" in args:
            kwargs["top_p"] = args["top_p"]

        if stream:
            response_iter = self.client.chat.completions.create(
                messages=cast(List[ChatCompletionMessageParam], messages),
                stream=True,
                model=args["model"],
                **kwargs,
            )

            for response in response_iter:
                next_choice = response.choices[0]
                if next_choice.finish_reason is None and next_choice.delta.content:
                    yield next_choice.delta.content
</section id="C">
<section id="D">
        else:
            response = self.client.chat.completions.create(
                messages=cast(List[ChatCompletionMessageParam], messages),
                model=args["model"],
                stream=False,
                **kwargs,
            )
            next_choice = response.choices[0]
            if next_choice.message.content:
</section id="D">
<section id="E">
                yield next_choice.message.content


def num_tokens_from_messages_openai(messages: List[Message], model: str) -> int:
    encoding = tiktoken.encoding_for_model(model)
    num_tokens = 0
    for message in messages:
        # every message follows <im_start>{role/name}\n{content}<im_end>\n
        num_tokens += 4
        for key, value in message.items():
            assert isinstance(value, str)
            num_tokens += len(encoding.encode(value))
            if key == "name":  # if there's a name, the role is omitted
                num_tokens += -1  # role is always required and always 1 token
    num_tokens += 2  # every reply is primed with <im_start>assistant
    return num_tokens


def num_tokens_from_completion_openai(completion: Message, model: str) -> int:
    return num_tokens_from_messages_openai([completion], model)

    async def process_image_input(self, image_path: str) -> str:
        try:
            response = await self.vision_handler.recognize_image(image_path=image_path)
            recognized_text = decode_response_to_text(response)
            return recognized_text
        except Exception as e:
            return f'Error processing image: {e}'
    return num_tokens_from_messages_openai([completion], model)
</section id="E">

You can continue to make changes to the code sections and call the `search_and_replace` function again.

# Repo & Issue Metadata
Repo: gpt-cmd: No description provided.
Issue Title: 增加支持多模态，有图片识别功能和图片生成功能

Issue Description: ### Details
## 增加支持多模态，有图片识别功能和图片生成功能

目前只是使用了chatgpt的[Chat Completions API](https://platform.openai.com/docs/guides/text-generation/chat-completions-api)接口。所以无法支持vision功能。我希望能够增加这个功能，根据用户的问题，判断是否需要调用dall-e-3和gpt-4-vision-preview来接受vision的内容和生成图片的功能。



**Summaries of links found in the content:**

https://platform.openai.com/docs/guides/text-generation/chat-completions-api)接口。所以无法支持vision功能。我希望能够增加这个功能，根据用户的问题，判断是否需要调用dall-e-3和gpt-4-vision-preview来接受vision的内容和生成图片的功能:

The page metadata indicates that the page is not accessible due to JavaScript being turned off and cookies not being enabled. Therefore, it is not possible to provide a summary of the page content or any code snippets.

You have previously changed these files:
<changed_file file_path="gptcli/vision.py">
--- 
+++ 
@@ -0,0 +1,40 @@
+import base64
+import json
+
+import aiohttp
+
+
+class VisionAPIHandler:
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        self.headers = {"Authorization": f"Bearer {api_key}"}
+
+    async def generate_image(self, prompt: str, n_images: int = 1):
+        async with aiohttp.ClientSession() as session:
+            payload = {"prompt": prompt, "n": n_images}
+            async with session.post("https://api.openai.com/v1/images/generations", headers=self.headers, json=payload) as response:
+                if response.status == 200:
+                    return await response.json()
+                else:
+                    raise Exception(f"Failed to generate image: {response.status}")
+
+    async def recognize_image(self, image_path: str):
+        image_base64 = encode_image_to_base64(image_path)
+        async with aiohttp.ClientSession() as session:
+            payload = {"image": image_base64}
+            async with session.post("https://api.openai.com/v1/vision/recognitions", headers=self.headers, json=payload) as response:
+                if response.status == 200:
+                    return await response.json()
+                else:
+                    raise Exception(f"Failed to recognize image: {response.status}")
+
+def encode_image_to_base64(image_path: str) -> str:
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+def decode_base64_to_image(base64_str: str, output_path: str):
+    with open(output_path, "wb") as output_file:
+        output_file.write(base64.b64decode(base64_str))
+
+def decode_response_to_text(response: dict) -> str:
+    return response.get("data", {}).get("text", "")
</changed_file>
<changed_file file_path="gptcli/cli_vision_commands.py">
--- 
+++ 
@@ -0,0 +1,46 @@
+import argparse
+import sys
+
+from gptcli.vision import VisionAPIHandler
+
+
+def cli_generate_image():
+    parser = argparse.ArgumentParser(description="Generate images from prompts.")
+    parser.add_argument("prompt", type=str, help="Prompt for generating images.")
+    parser.add_argument("--n_images", type=int, default=1, help="Number of images to generate.")
+    args = parser.parse_args()
+
+    try:
+        vision_handler = VisionAPIHandler(api_key="Your_API_Key_Here")
+        response = vision_handler.generate_image(prompt=args.prompt, n_images=args.n_images)
+        for image_data in response["data"]:
+            print(f"Generated Image URL: {image_data['url']}")
+    except Exception as e:
+        print(f"Error generating image: {e}")
+        sys.exit(1)
+
+def cli_recognize_image():
+    parser = argparse.ArgumentParser(description="Recognize text from an image.")
+    parser.add_argument("image_path", type=str, help="Path to the image file.")
+    args = parser.parse_args()
+
+    try:
+        vision_handler = VisionAPIHandler(api_key="Your_API_Key_Here")
+        response = vision_handler.recognize_image(image_path=args.image_path)
+        print(f"Recognized Text: {response['data']['text']}")
+    except Exception as e:
+        print(f"Error recognizing image: {e}")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="CLI for image recognition and generation.")
+    parser.add_argument("--generate", action="store_true", help="Generate images from prompts.")
+    parser.add_argument("--recognize", action="store_true", help="Recognize text from an image.")
+    args = parser.parse_args()
+
+    if args.generate:
+        cli_generate_image()
+    elif args.recognize:
+        cli_recognize_image()
+    else:
+        parser.print_help()
</changed_file>
<changed_file file_path="gptcli/config.py">
--- 
+++ 
@@ -43,4 +43,6 @@
         config = yaml.safe_load(file)
         return GptCliConfig(
             **config,
-        )+        )
+    dalle_api_key: Optional[str] = os.environ.get("DALLE_API_KEY")
+    gpt4_vision_api_key: Optional[str] = os.environ.get("GPT4_VISION_API_KEY")
</changed_file>
<changed_file file_path="gptcli/openai.py">
--- 
+++ 
@@ -22,6 +22,24 @@
             kwargs["top_p"] = args["top_p"]
 
         if stream:
+from gptcli.vision import VisionAPIHandler
+
+        self.vision_handler = VisionAPIHandler(api_key=openai.api_key)
+        if input_type == "image":
+            if "image_path" in args:
+                try:
+                    response = await self.vision_handler.recognize_image(image_path=args["image_path"])
+                    yield decode_response_to_text(response)
+                except Exception as e:
+                    yield f"Error recognizing image: {e}"
+            elif "prompt" in args:
+                try:
+                    response = await self.vision_handler.generate_image(prompt=args["prompt"], n_images=args.get("n_images", 1))
+                    for image_data in response["data"]:
+                        yield f"Generated Image URL: {image_data['url']}"
+                except Exception as e:
+                    yield f"Error generating image: {e}"
+        elif input_type == "text" and stream:
             response_iter = self.client.chat.completions.create(
                 messages=cast(List[ChatCompletionMessageParam], messages),
                 stream=True,
@@ -61,4 +79,5 @@
 
 
 def num_tokens_from_completion_openai(completion: Message, model: str) -> int:
-    return num_tokens_from_messages_openai([completion], model)+    return num_tokens_from_messages_openai([completion], model)
+from gptcli.vision import decode_response_to_text
</changed_file>

Relevant files in this PR:

<relevant_file file_path="gptcli/vision.py">
import base64
import json

import aiohttp


class VisionAPIHandler:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.headers = {"Authorization": f"Bearer {api_key}"}

    async def generate_image(self, prompt: str, n_images: int = 1):
        async with aiohttp.ClientSession() as session:
            payload = {"prompt": prompt, "n": n_images}
            async with session.post("https://api.openai.com/v1/images/generations", headers=self.headers, json=payload) as response:
                if response.status == 200:
                    return await response.json()
                else:
                    raise Exception(f"Failed to generate image: {response.status}")

    async def recognize_image(self, image_path: str):
        image_base64 = encode_image_to_base64(image_path)
        async with aiohttp.ClientSession() as session:
            payload = {"image": image_base64}
            async with session.post("https://api.openai.com/v1/vision/recognitions", headers=self.headers, json=payload) as response:
                if response.status == 200:
                    return await response.json()
                else:
                    raise Exception(f"Failed to recognize image: {response.status}")

def encode_image_to_base64(image_path: str) -> str:
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

def decode_base64_to_image(base64_str: str, output_path: str):
    with open(output_path, "wb") as output_file:
        output_file.write(base64.b64decode(base64_str))

def decode_response_to_text(response: dict) -> str:
    return response.get("data", {}).get("text", "")

</relevant_file>

<section id="A">
import os
from typing import Iterator, List
import anthropic

from gptcli.completion import CompletionProvider, Message

api_key = os.environ.get("ANTHROPIC_API_KEY")


def get_client():
    if not api_key:
        raise ValueError("ANTHROPIC_API_KEY environment variable not set")

    return anthropic.Anthropic(api_key=api_key)


def role_to_name(role: str) -> str:
    if role == "system" or role == "user":
        return anthropic.HUMAN_PROMPT
    elif role == "assistant":
        return anthropic.AI_PROMPT
    else:
        raise ValueError(f"Unknown role: {role}")


</section id="A">
<section id="B">
def make_prompt(messages: List[Message]) -> str:
    prompt = "\n".join(
        [f"{role_to_name(message['role'])}{message['content']}" for message in messages]
    )
    prompt += f"{role_to_name('assistant')}"
    return prompt


class AnthropicCompletionProvider(CompletionProvider):
</section id="B">
<section id="C">
    def complete(
        self, messages: List[Message], args: dict, stream: bool = False
    ) -> Iterator[str]:
        kwargs = {
            "prompt": make_prompt(messages),
            "stop_sequences": [anthropic.HUMAN_PROMPT],
            "max_tokens_to_sample": 4096,
            "model": args["model"],
        }
        if "temperature" in args:
            kwargs["temperature"] = args["temperature"]
        if "top_p" in args:
            kwargs["top_p"] = args["top_p"]

        client = get_client()
        if stream:
            response = client.completions.create(**kwargs, stream=True)
        else:
            response = [client.completions.create(**kwargs, stream=False)]

        for data in response:
            next_completion = data.completion
            yield next_completion


def num_tokens_from_messages_anthropic(messages: List[Message], model: str) -> int:
    prompt = make_prompt(messages)
    client = get_client()
    return client.count_tokens(prompt)


def num_tokens_from_completion_anthropic(message: Message, model: str) -> int:
    client = get_client()
    return client.count_tokens(message["content"])
</section id="C">

# Request
• Similar to the modifications in `openai.py`, integrate calls to `VisionAPIHandler` for image processing functionalities.
• Ensure that the `AnthropicCompletionProvider` can handle both text and image inputs, directing each to the appropriate API (textual or vision) based on the input type.

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -47,14 +47,33 @@
             kwargs["top_p"] = args["top_p"]
 
         client = get_client()
-        if stream:
-            response = client.completions.create(**kwargs, stream=True)
-        else:
-            response = [client.completions.create(**kwargs, stream=False)]
+        from gptcli.vision import VisionAPIHandler, decode_response_to_text
 
-        for data in response:
-            next_completion = data.completion
-            yield next_completion
+        vis_handler = VisionAPIHandler(api_key=api_key)
+
+        if args["input_type"] == "image":
+            if "image_path" in args:
+                try:
+                    response = vis_handler.recognize_image(image_path=args["image_path"])
+                    yield decode_response_to_text(response)
+                except Exception as e:
+                    yield f"Error recognizing image: {e}"
+            elif "prompt" in args:
+                try:
+                    response = vis_handler.generate_image(prompt=args["prompt"], n_images=args.get("n_images", 1))
+                    for image_data in response["data"]:
+                        yield f"Generated Image URL: {image_data['url']}"
+                except Exception as e:
+                    yield f"Error generating image: {e}"
+        elif args["input_type"] == "text":
+            if stream:
+                response = client.completions.create(**kwargs, stream=True)
+            else:
+                response = [client.completions.create(**kwargs, stream=False)]
+
+            for data in response:
+                next_completion = data.completion
+                yield next_completion
 
 
 def num_tokens_from_messages_anthropic(messages: List[Message], model: str) -> int:

```
Here are the new code sections:




<section id="A">
import os
from typing import Iterator, List
import anthropic

from gptcli.completion import CompletionProvider, Message

api_key = os.environ.get("ANTHROPIC_API_KEY")


def get_client():
    if not api_key:
        raise ValueError("ANTHROPIC_API_KEY environment variable not set")

    return anthropic.Anthropic(api_key=api_key)


def role_to_name(role: str) -> str:
    if role == "system" or role == "user":
        return anthropic.HUMAN_PROMPT
    elif role == "assistant":
        return anthropic.AI_PROMPT
    else:
        raise ValueError(f"Unknown role: {role}")


</section id="A">
<section id="B">
def make_prompt(messages: List[Message]) -> str:
    prompt = "\n".join(
        [f"{role_to_name(message['role'])}{message['content']}" for message in messages]
    )
    prompt += f"{role_to_name('assistant')}"
    return prompt


class AnthropicCompletionProvider(CompletionProvider):
</section id="B">
<section id="C">
    def complete(
        self, messages: List[Message], args: dict, stream: bool = False
    ) -> Iterator[str]:
        kwargs = {
            "prompt": make_prompt(messages),
            "stop_sequences": [anthropic.HUMAN_PROMPT],
            "max_tokens_to_sample": 4096,
            "model": args["model"],
        }
        if "temperature" in args:
            kwargs["temperature"] = args["temperature"]
        if "top_p" in args:
            kwargs["top_p"] = args["top_p"]

        client = get_client()
        from gptcli.vision import VisionAPIHandler, decode_response_to_text

        vis_handler = VisionAPIHandler(api_key=api_key)

</section id="C">
<section id="D">
        if args["input_type"] == "image":
            if "image_path" in args:
                try:
                    response = vis_handler.recognize_image(image_path=args["image_path"])
                    yield decode_response_to_text(response)
                except Exception as e:
                    yield f"Error recognizing image: {e}"
            elif "prompt" in args:
                try:
                    response = vis_handler.generate_image(prompt=args["prompt"], n_images=args.get("n_images", 1))
                    for image_data in response["data"]:
                        yield f"Generated Image URL: {image_data['url']}"
                except Exception as e:
                    yield f"Error generating image: {e}"
</section id="D">
<section id="E">
        elif args["input_type"] == "text":
            if stream:
                response = client.completions.create(**kwargs, stream=True)
            else:
                response = [client.completions.create(**kwargs, stream=False)]

            for data in response:
                next_completion = data.completion
                yield next_completion


def num_tokens_from_messages_anthropic(messages: List[Message], model: str) -> int:
    prompt = make_prompt(messages)
    client = get_client()
    return client.count_tokens(prompt)


def num_tokens_from_completion_anthropic(message: Message, model: str) -> int:
    client = get_client()
    return client.count_tokens(message["content"])
</section id="E">

You can continue to make changes to the code sections and call the `search_and_replace` function again.