Working Open AI version

app.py

@@ -119,6 +119,9 @@ class Gpt4AllWebUI(GPT4AllAPI):
         
         self.add_endpoint("/", "", self.index, methods=["GET"])
         self.add_endpoint("/<path:filename>", "serve_static", self.serve_static, methods=["GET"])
+        
+        self.add_endpoint("/images/<path:filename>", "serve_images", self.serve_images, methods=["GET"])
+        self.add_endpoint("/bindings/<path:filename>", "serve_bindings", self.serve_bindings, methods=["GET"])
         self.add_endpoint("/personalities/<path:filename>", "serve_personalities", self.serve_personalities, methods=["GET"])
         self.add_endpoint("/outputs/<path:filename>", "serve_outputs", self.serve_outputs, methods=["GET"])
 
@@ -452,7 +455,7 @@ class Gpt4AllWebUI(GPT4AllAPI):
                     self.binding = self.process.load_binding(self.config["binding"], install=True)
 
                 except Exception as ex:
-                    print("Couldn't build binding")
+                    print(f"Couldn't build binding: [{ex}]")
                     return jsonify({'setting_name': data['setting_name'], "status":False, 'error':str(ex)})
             else:
                 if self.config["debug"]:
@@ -508,6 +511,10 @@ class Gpt4AllWebUI(GPT4AllAPI):
                 try:
                     bnd = load_config(card)
                     bnd["folder"]=f.stem
+                    icon_path = Path(f/"logo.png")
+                    if icon_path.exists():
+                        bnd["icon"]=str(icon_path)
+
                     bindings.append(bnd)
                 except Exception as ex:
                     print(f"Couldn't load backend card : {f}\n\t{ex}")
@@ -610,6 +617,21 @@ class Gpt4AllWebUI(GPT4AllAPI):
         fn = filename.split("/")[-1]
         return send_from_directory(path, fn)
 
+    
+    def serve_images(self, filename):
+        root_dir = os.getcwd()
+        path = os.path.join(root_dir, 'images/')+"/".join(filename.split("/")[:-1])
+                            
+        fn = filename.split("/")[-1]
+        return send_from_directory(path, fn)
+    
+    def serve_bindings(self, filename):
+        root_dir = os.getcwd()
+        path = os.path.join(root_dir, 'bindings/')+"/".join(filename.split("/")[:-1])
+                            
+        fn = filename.split("/")[-1]
+        return send_from_directory(path, fn)
+
     def serve_personalities(self, filename):
         root_dir = os.getcwd()
         path = os.path.join(root_dir, 'personalities/')+"/".join(filename.split("/")[:-1])
@@ -838,12 +860,13 @@ class Gpt4AllWebUI(GPT4AllAPI):
                 owner_link = model.get("owner_link", 'https://github.com/ParisNeo')
                 filesize = int(model.get('filesize',0))
                 description = model.get('description',"")
+                model_type = model.get("model_type","")
                 if server.endswith("/"):
                     path = f'{server}{filename}'
                 else:
                     path = f'{server}/{filename}'
                 local_path = Path(f'./models/{self.config["binding"]}/{filename}')
-                is_installed = local_path.exists()
+                is_installed = local_path.exists() or model_type.lower()=="api"
                 models.append({
                     'title': filename,
                     'icon': image_url,  # Replace with the path to the model icon
@@ -854,6 +877,7 @@ class Gpt4AllWebUI(GPT4AllAPI):
                     'isInstalled': is_installed,
                     'path': path,
                     'filesize': filesize,
+                    'model_type': model_type
                 })
             except Exception as ex:
                 print("#################################")

bindings/backend_template/__init__.py

@@ -57,7 +57,7 @@ class CustomBinding(LLMBinding):
         Returns:
             list: A list of tokens representing the tokenized prompt.
         """
-        return self.model.tokenize(prompt.encode())
+        return None
 
     def detokenize(self, tokens_list):
         """
@@ -69,7 +69,7 @@ class CustomBinding(LLMBinding):
         Returns:
             str: The detokenized text as a string.
         """
-        return self.model.detokenize(tokens_list)
+        return None
     
     def generate(self, 
                  prompt:str,                  
@@ -87,17 +87,22 @@ class CustomBinding(LLMBinding):
         """
         try:
             output = ""
-            self.model.reset()
-            tokens = self.model.tokenize(prompt)
             count = 0
             generated_text = """
 This is an empty binding that shows how you can build your own binding.
-Find it in bindings
+Find it in bindings.
+```python
+# This is a python snippet
+print("Hello World")
+```
+
+This is a photo
+![](/images/icon.png)
 """
-            for tok in re.split(r' |\n', generated_text):               
+            for tok in re.split(r'(\s+)', generated_text):               
-                if count >= n_predict or self.model.is_eos_token(tok):
+                if count >= n_predict:
                     break
-                word = self.model.detokenize(tok)
+                word = tok
                 if new_text_callback is not None:
                     if not new_text_callback(word):
                         break

bindings/backend_template/models.yaml

@@ -5,3 +5,4 @@
   owner: Owner_name
   server: https://Path_to_the_server_to_load_from
   sha256: The Hash code of the file
+  model_type: api # api or file

bindings/c_transformers/__init__.py

@@ -106,10 +106,10 @@ class CTRansformers(LLMBinding):
             count = 0
             for tok in self.model.generate(
                                             tokens,
-                                            top_k=self.config['top_k'],
+                                            top_k=gpt_params['top_k'],
-                                            top_p=self.config['top_p'],
+                                            top_p=gpt_params['top_p'],
-                                            temperature=self.config['temperature'],
+                                            temperature=gpt_params['temperature'],
-                                            repetition_penalty=self.config['repeat_penalty'],
+                                            repetition_penalty=gpt_params['repeat_penalty'],
                                             seed=self.config['seed'],
                                             batch_size=1,
                                             threads = self.config['n_threads'],

bindings/config_local.yaml

@@ -0,0 +1 @@
+pdf_file_path: ''

bindings/gpt_4all/__init__.py

@@ -96,10 +96,10 @@ class GPT4ALL(LLMBinding):
             self.model._response_callback = local_callback
             self.model.generate(prompt, 
                                            n_predict=n_predict,                                           
-                                            temp=self.config['temperature'],
+                                            temp=gpt_params["temp"],
-                                            top_k=self.config['top_k'],
+                                            top_k=gpt_params['top_k'],
-                                            top_p=self.config['top_p'],
+                                            top_p=gpt_params['top_p'],
-                                            repeat_penalty=self.config['repeat_penalty'],
+                                            repeat_penalty=gpt_params['repeat_penalty'],
                                             repeat_last_n = self.config['repeat_last_n'],
                                             # n_threads=self.config['n_threads'],
                                             streaming=False,

bindings/gpt_j_a/__init__.py

@@ -80,10 +80,10 @@ class GptJ(LLMBinding):
             output = ""
             for tok in self.model.generate(prompt, 
                                            n_predict=n_predict,                                           
-                                            temp=self.config['temperature'],
+                                            temp=gpt_params["temp"],
-                                            top_k=self.config['top_k'],
+                                            top_k=gpt_params['top_k'],
-                                            top_p=self.config['top_p'],
+                                            top_p=gpt_params['top_p'],
-                                            #repeat_penalty=self.config['repeat_penalty'],
+                                            #repeat_penalty=gpt_params['repeat_penalty'],
                                             #repeat_last_n = self.config['repeat_last_n'],
                                             n_threads=self.config['n_threads'],
                                            ):

bindings/gpt_j_m/__init__.py

@@ -83,10 +83,10 @@ class GPTJ(LLMBinding):
                                             seed=self.config['seed'],
                                             n_threads=self.config['n_threads'],
                                             n_predict=n_predict,
-                                            top_k=self.config['top_k'],
+                                            top_k=gpt_params['top_k'],
-                                            top_p=self.config['top_p'],
+                                            top_p=gpt_params['top_p'],
-                                            temp=self.config['temperature'],
+                                            temp=gpt_params["temp"],
-                                            repeat_penalty=self.config['repeat_penalty'],
+                                            repeat_penalty=gpt_params['repeat_penalty'],
                                             repeat_last_n=self.config['repeat_last_n'],
                                             n_batch=8,
                                             reset=True,

bindings/llama_cpp_official/__init__.py

@@ -87,10 +87,10 @@ class LLAMACPP(LLMBinding):
             tokens = self.model.tokenize(prompt.encode())
             count = 0
             for tok in self.model.generate(tokens, 
-                                            temp=self.config['temperature'],
+                                            temp=gpt_params["temp"],
-                                            top_k=self.config['top_k'],
+                                            top_k=gpt_params['top_k'],
-                                            top_p=self.config['top_p'],
+                                            top_p=gpt_params['top_p'],
-                                            repeat_penalty=self.config['repeat_penalty'],
+                                            repeat_penalty=gpt_params['repeat_penalty'],
                                            ):
                 if count >= n_predict or (tok == self.model.token_eos()):
                     break

bindings/open_ai/__init__.py

@@ -42,8 +42,10 @@ class OpenAIGPT(LLMBinding):
         # The local config can be used to store personal information that shouldn't be shared like chatgpt Key 
         # or other personal information
         # This file is never commited to the repository as it is ignored by .gitignore
+        self.config = config
         self._local_config_file_path = Path(__file__).parent/"config_local.yaml"
-        self.config = load_config(self._local_config_file_path)
+        self.local_config = load_config(self._local_config_file_path)
+        openai.api_key = self.local_config["openai_key"]
 
         # Do your initialization stuff
             
@@ -57,7 +59,7 @@ class OpenAIGPT(LLMBinding):
         Returns:
             list: A list of tokens representing the tokenized prompt.
         """
-        return self.model.tokenize(prompt.encode())
+        return None
 
     def detokenize(self, tokens_list):
         """
@@ -69,7 +71,7 @@ class OpenAIGPT(LLMBinding):
         Returns:
             str: The detokenized text as a string.
         """
-        return self.model.detokenize(tokens_list)
+        return None
     
     def generate(self, 
                  prompt:str,                  
@@ -86,35 +88,21 @@ class OpenAIGPT(LLMBinding):
             verbose (bool, optional): If true, the code will spit many informations about the generation process. Defaults to False.
         """
         try:
-            output = ""
-            self.model.reset()
-            tokens = self.model.tokenize(prompt)
-            count = 0
-            generated_text = """
-This is an empty binding that shows how you can build your own binding.
-Find it in bindings
-"""
-
             response = openai.Completion.create(
-                engine='text-davinci-003',  # Choose the engine according to your OpenAI plan
+                engine=self.config["model"],  # Choose the engine according to your OpenAI plan
                 prompt=prompt,
-                max_tokens=100,  # Adjust the desired length of the generated response
+                max_tokens=n_predict,  # Adjust the desired length of the generated response
                 n=1,  # Specify the number of responses you want
                 stop=None,  # Define a stop sequence if needed
-                temperature=0.7  # Adjust the temperature for more or less randomness in the output
+                temperature=gpt_params["temp"]  # Adjust the temperature for more or less randomness in the output
             )
-            for tok in re.split(r' |\n', generated_text):               
+
-                if count >= n_predict or self.model.is_eos_token(tok):
+            # Extract the generated reply from the API response
-                    break
+            reply = response.choices[0].text.strip()
-                word = self.model.detokenize(tok)
+            return reply
-                if new_text_callback is not None:
-                    if not new_text_callback(word):
-                        break
-                output += word
-                count += 1
         except Exception as ex:
             print(ex)
-        return output            
+        return ""            
 
     
     @staticmethod

bindings/open_ai/install.py

@@ -3,7 +3,7 @@ from pathlib import Path
 import requests
 from tqdm import tqdm
 from api.config import save_config
-
+import yaml
 class Install:
     def __init__(self, api):
         # Get the current directory
@@ -11,7 +11,7 @@ class Install:
         install_file = current_dir / ".installed"
 
         if not install_file.exists():
-            print("-------------- Template binding -------------------------------")
+            print("-------------- OpenAI Binding -------------------------------")
             print("This is the first time you are using this binding.")
             print("Installing ...")
             # Step 2: Install dependencies using pip from requirements.txt
@@ -24,14 +24,12 @@ class Install:
 
             #Create 
             self._local_config_file_path = Path(__file__).parent/"config_local.yaml"
-            if not self._local_config_file_path.exists:
+            if not self._local_config_file_path.exists():
                 key = input("Please enter your Open AI Key")
                 config={
                     "openai_key":key
                 }
                 self.config = save_config(config, self._local_config_file_path)
-
-            
             #Create the install file (a file that is used to insure the installation was done correctly)
             with open(install_file,"w") as f:
                 f.write("ok")
@@ -42,3 +40,23 @@ class Install:
         """        
         subprocess.run(["pip", "install", "torch", "torchvision", "torchaudio", "--no-cache-dir", "--index-url", "https://download.pytorch.org/whl/cu117"])
 
+
+    def create_config_file(self):
+        """
+        Create a config_local.yaml file with predefined data.
+
+        The function creates a config_local.yaml file with the specified data. The file is saved in the parent directory
+        of the current file.
+
+        Args:
+            None
+
+        Returns:
+            None
+        """
+        data = {
+            "pdf_file_path":  "" # Path to the PDF that will be discussed
+        }
+        path = Path(__file__).parent.parent / 'config_local.yaml'
+        with open(path, 'w') as file:
+            yaml.dump(data, file)

bindings/open_ai/models.yaml

@@ -1,8 +1,39 @@
-- description: ChatGPT model
+- description: Most advanced language model by OpenAI based on GPT-3 architecture, offering powerful language generation capabilities.
-  icon: https://www.google.fr/url?sa=i&url=https%3A%2F%2Fcommons.wikimedia.org%2Fwiki%2FFile%3AChatGPT_logo.svg&psig=AOvVaw1rUG9Bl0WfHOYRJF7LgSmA&ust=1685107628710000&source=images&cd=vfe&ved=0CBEQjRxqFwoTCJDr0J7JkP8CFQAAAAAdAAAAABAE
+  icon: /bindings/open_ai/logo.png
-  filename: ChatGpt by Open AI
+  filename: gpt-3.5-turbo
   license: Commercial
   owner_link: https://link_to_the_owner_web_page
   owner: Open AI
   server: https://openai.com
-  sha256: NONE
+  sha256: ~
+  model_type: api
+
+- description: Highly capable language model for generating high-quality text and performing various natural language processing tasks.
+  icon: /bindings/open_ai/logo.png
+  filename: text-davinci-003
+  license: Commercial
+  owner_link: https://link_to_the_owner_web_page
+  owner: Open AI
+  server: https://openai.com
+  sha256: ~
+  model_type: api
+
+- description: Earlier version of the text-davinci model, offering similar functionality with potentially slightly different performance characteristics.
+  icon: /bindings/open_ai/logo.png
+  filename: text-davinci-002
+  license: Commercial
+  owner_link: https://link_to_the_owner_web_page
+  owner: Open AI
+  server: https://openai.com
+  sha256: ~
+  model_type: api
+
+- description: Original version of the text-davinci model, providing strong language generation capabilities.
+  icon: /bindings/open_ai/logo.png
+  filename: text-davinci-001
+  license: Commercial
+  owner_link: https://link_to_the_owner_web_page
+  owner: Open AI
+  server: https://openai.com
+  sha256: ~
+  model_type: api

bindings/open_ai/requirements.txt

@@ -1 +1,2 @@
 openai
+tiktoken 

bindings/py_llama_cpp/__init__.py

@@ -83,10 +83,10 @@ class PyLLAMACPP(LLMBinding):
             output = ""
             for tok in self.model.generate(prompt, 
                                            n_predict=n_predict,                                           
-                                            temp=self.config['temperature'],
+                                            temp=gpt_params['temperature'],
-                                            top_k=self.config['top_k'],
+                                            top_k=gpt_params['top_k'],
-                                            top_p=self.config['top_p'],
+                                            top_p=gpt_params['top_p'],
-                                            repeat_penalty=self.config['repeat_penalty'],
+                                            repeat_penalty=gpt_params['repeat_penalty'],
                                             repeat_last_n = self.config['repeat_last_n'],
                                             n_threads=self.config['n_threads'],
                                            ):

docs/youtube/script_models.md

@@ -4,4 +4,4 @@ In this short video we will look at bindings and models, how to select them and
 
 First, to select a binding, go to the settings tab then Bindings zoo.
 
-You will find a certain numbre of 
+You will find a certain numbre of bindings. Each binding as built by a person or a team. Make sure to visit their

web/src/components/BindingEntry.vue

@@ -31,6 +31,11 @@
           <b>Version:&nbsp;</b>
           {{ binding.version }}
         </div>
+        <a :href="binding.link" target="_blank" class="flex items-center">
+          <i data-feather="github" class="w-5 m-1"></i>
+          <b>Link:&nbsp;</b>
+          {{ binding.link }}
+        </a>
       </div>
       <div class="flex items-center">
         <i data-feather="info" class="w-5 m-1"></i>

web/src/components/ModelEntry.vue

@@ -61,7 +61,7 @@
       <p class="mx-1 opacity-80">{{ description }}</p>
     </div>
     <div class="flex-shrink-0" >
-      <button class="px-4 py-2 rounded-md text-white font-bold transition-colors duration-300"
+      <button v-if="model_type !== 'api'" class="px-4 py-2 rounded-md text-white font-bold transition-colors duration-300"
         :class="[isInstalled ? 'bg-red-500 hover:bg-red-600' : linkNotValid ? 'bg-gray-500 hover:bg-gray-600' : 'bg-green-500 hover:bg-green-600']"
         :disabled="installing || uninstalling" @click.stop="toggleInstall">
         <template v-if="installing">
@@ -84,6 +84,7 @@
           {{ isInstalled ? model.isCustomModel ? 'Delete' : 'Uninstall' : linkNotValid ? 'Link is not valid':'Install' }}
         </template>
       </button>
+
     </div>
 
   </div>
@@ -110,7 +111,8 @@ export default {
     onUninstall: Function,
     onSelected: Function,
     selected: Boolean,
-    model: Object
+    model: Object,
+    model_type: String
   },
   data() {
     return {
@@ -136,8 +138,9 @@ export default {
             return filesize(size)
         },
     async getFileSize(url) {
+      console.log(this.model_type);
+      if(this.model_type!="api"){
         try {
-
           const res = await axios.head(url)
           //console.log("addddd",url, res.headers)
           if (res) {
@@ -183,6 +186,8 @@ export default {
           return 'Could not be determined'
 
           }
+      }
+
 
     },
     getImgUrl() {

web/src/views/SettingsView.vue

@@ -179,7 +179,7 @@
                                     :title="model.title" :icon="model.icon" :path="model.path" :owner="model.owner"
                                     :owner_link="model.owner_link" :license="model.license" :description="model.description"
                                     :is-installed="model.isInstalled" :on-install="onInstall" :on-uninstall="onUninstall"
-                                    :on-selected="onSelected" :selected="model.title === configFile.model" :model="model" />
+                                    :on-selected="onSelected" :selected="model.title === configFile.model" :model="model" :model_type="model.model_type" />
                             </TransitionGroup>
                         </div>
                     </div>