uploaded some files

HuggingChatAPI.py

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+from hugchat import hugchat
+
+import time 
+
+from typing import Any, List, Mapping, Optional
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+
+
+# THIS IS A CUSTOM LLM WRAPPER Based on hugchat library
+# Reference :
+# - Langchain custom LLM wrapper : https://python.langchain.com/docs/modules/model_io/models/llms/how_to/custom_llm
+# - HugChat library : https://github.com/Soulter/hugging-chat-api
+# - I am Alessandro Ciciarelli the owner of IntelligenzaArtificialeItalia.net , my dream is to democratize AI and make it accessible to everyone.
+
+class HuggingChat(LLM):
+
+    """HuggingChat LLM wrapper."""
+
+    chatbot : Optional[hugchat.ChatBot] = None
+
+
+    email: Optional[str] = None
+    psw: Optional[str] = None
+    cookie_path : Optional[str] = None
+
+    conversation : Optional[str] = None
+    model: Optional[int] = 0 # 0 = OpenAssistant/oasst-sft-6-llama-30b-xor , 1 = meta-llama/Llama-2-70b-chat-hf
+
+    temperature: Optional[float] = 0.9
+    top_p: Optional[float] = 0.95
+    repetition_penalty: Optional[float] = 1.2
+    top_k: Optional[int] = 50
+    truncate: Optional[int] = 1024
+    watermark: Optional[bool] = False
+    max_new_tokens: Optional[int] = 1024
+    stop: Optional[list] = ["</s>"]
+    return_full_text: Optional[bool] = False
+    stream_resp: Optional[bool] = True
+    use_cache: Optional[bool] = False
+    is_retry: Optional[bool] = False
+    retry_count: Optional[int] = 5
+
+    avg_response_time: float = 0.0
+    log : Optional[bool] = False
+    
+    
+    @property
+    def _llm_type(self) -> str:
+        return "🤗CUSTOM LLM WRAPPER Based on hugging-chat-api library"
+    
+
+    def create_chatbot(self) -> None:
+        if not any([self.email, self.psw, self.cookie_path]):
+            raise ValueError("email, psw, or cookie_path is required.")
+        
+        try:
+            if self.email and self.psw:
+                # Create a ChatBot using email and psw
+                from hugchat.login import Login
+                start_time = time.time()
+                sign = Login(self.email, self.psw)
+                cookies = sign.login()
+                end_time = time.time()
+                if self.log : print(f"\n[LOG] Login successfull in {round(end_time - start_time)} seconds")
+            else:
+                # Create a ChatBot using cookie_path
+                cookies = self.cookie_path and hugchat.ChatBot(cookie_path=self.cookie_path)
+            
+            self.chatbot = cookies.get_dict() and hugchat.ChatBot(cookies=cookies.get_dict())
+            if self.log : print(f"[LOG] LLM WRAPPER created successfully")
+            
+        except Exception as e:
+            raise ValueError("LogIn failed. Please check your credentials or cookie_path. " + str(e))
+
+        # Setup ChatBot info
+        self.chatbot.switch_llm(self.model)
+        if self.log : print(f"[LOG] LLM WRAPPER switched to model { 'OpenAssistant/oasst-sft-6-llama-30b-xor' if self.model == 0 else 'meta-llama/Llama-2-70b-chat-hf'}")
+
+        self.conversation = self.conversation or self.chatbot.new_conversation()
+        self.chatbot.change_conversation(self.conversation)
+        if self.log : print(f"[LOG] LLM WRAPPER changed conversation to {self.conversation}\n")
+        
+
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        if stop:
+            raise ValueError("stop kwargs are not permitted.")
+        
+        self.create_chatbot() if not self.chatbot else None
+        
+        try:
+            if self.log : print(f"[LOG] LLM WRAPPER called with prompt: {prompt}")
+            start_time = time.time()
+            resp = self.chatbot.chat(
+                prompt,
+                temperature=self.temperature,
+                top_p=self.top_p,
+                repetition_penalty=self.repetition_penalty,
+                top_k=self.top_k,
+                truncate=self.truncate,
+                watermark=self.watermark,
+                max_new_tokens=self.max_new_tokens,
+                stop=self.stop,
+                return_full_text=self.return_full_text,
+                stream=self.stream_resp,
+                use_cache=self.use_cache,
+                is_retry=self.is_retry,
+                retry_count=self.retry_count,
+            )
+
+            end_time = time.time()
+            
+            self.avg_response_time = (self.avg_response_time + (end_time - start_time)) / 2 if self.avg_response_time else end_time - start_time
+            
+            if self.log : print(f"[LOG] LLM WRAPPER response time: {round(end_time - start_time)} seconds")
+            if self.log : print(f"[LOG] LLM WRAPPER avg response time: {round(self.avg_response_time)} seconds")
+            if self.log : print(f"[LOG] LLM WRAPPER response: {resp}\n\n")
+
+            return str(resp)
+            
+        except Exception as e:
+            raise ValueError("ChatBot failed, please check your parameters. " + str(e))
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        parms = { 
+            "model": "HuggingChat",
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "repetition_penalty": self.repetition_penalty,
+            "top_k": self.top_k,
+            "truncate": self.truncate,
+            "watermark": self.watermark,
+            "max_new_tokens": self.max_new_tokens,
+            "stop": self.stop,
+            "return_full_text": self.return_full_text,
+            "stream": self.stream_resp,
+            "use_cache": self.use_cache,
+            "is_retry": self.is_retry,
+            "retry_count": self.retry_count,
+            "avg_response_time": self.avg_response_time,
+        }
+        return parms
+    
+    @property
+    def _get_avg_response_time(self) -> float:
+        """Get the average response time."""
+        return self.avg_response_time
+
+
+
+#HOW TO USE IT
+# 1) Install the library : pip install -U hugchat langchain
+# 2) Get your HuggingFace credentials : https://huggingface.co/
+# 3) Import the library and enjoy it : from HCA import HCA 
+
+# EXAMPLE 1 : Using email and psw
+# llm = HCA(email="YOUR_EMAIL", psw="YOUR_psw")
+
+# EXAMPLE 2 : Using cookie file
+# llm = HCA(cookie_path="YOUR_COOKIE_PATH")
+
+# EXAMPLE 3 : Modify the default parameters
+# llm = HCA(email="YOUR_EMAIL", psw="YOUR_psw", log=True , model=1, temperature=0.9, top_p=0.95, repetition_penalty=1.2, top_k=50, truncate=1024, watermark=False, max_new_tokens=1024, stop=["</s>"], return_full_text=False, stream=True, use_cache=False, is_retry=False, retry_count=5)
+
+# EXAMPLE 4 : Using the LLM
+# print(llm("Hello, how are you?"))
+
+
+# EXAMPLE 5 : simple use 
+# from HCA import HCA
+#llm = HCA(email="YOUR_EMAIL", psw="YOUR_psw" , log=True, model=1)
+#txt = input("\n\nYou (write 'exit' for stop): ")
+#while txt != "exit":
+    #print("Bot : " + llm(txt) + "\n")
+    #print("Avg response time : " + str(llm._get_avg_response_time))
+    #txt = input("You : ")
+
+
+
+#from hugchat import hugchat
+#from hugchat.login import Login
+#from langchain.llms.base import LLM
+#from typing import Optional, List, Mapping, Any
+#from time import sleep
+
+
+# THIS IS A CUSTOM LLM WRAPPER Based on hugchat library
+# Reference :
+# - Langchain custom LLM wrapper : https://python.langchain.com/docs/modules/model_io/models/llms/how_to/custom_llm
+# - HugChat library : https://github.com/Soulter/hugging-chat-api
+
+
+
+
+
+#llm = HuggingChat(email = "YOUR-EMAIL" , psw = = "YOUR-PSW" ) #for start new chat
+
+
+#print(llm("Hello, how are you?"))
+#print(llm("what is AI?"))
+#print(llm("Can you resume your previus answer?")) #now memory work well
+

exportchat.py

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+"""
+PYTHON FILE FOR EXPORT CHAT FUNCTION
+"""
+
+import streamlit as st
+from datetime import datetime
+
+
+def export_chat():
+    if 'generated' in st.session_state:
+        # save message in reverse order frist message always bot
+        # the chat is stored in a html file format
+        html_chat = ""
+        html_chat += '<html><head><title>ChatBOT Intelligenza Artificiale Italia 🧠🤖🇮🇹</title>'
+        # create two simply css box for bot and user like whatsapp
+        html_chat += '<style> .bot { background-color: #e5e5ea; padding: 10px; border-radius: 10px; margin: 10px; width: 50%; float: left; } .user { background-color: #dcf8c6; padding: 10px; border-radius: 10px; margin: 10px; width: 50%; float: right; } </style>'
+        html_chat += '</head><body>'
+        # add header
+        html_chat += '<center><h1>ChatBOT Intelligenza Artificiale Italia 🧠🤖🇮🇹</h1>'
+        # add link for danation
+        html_chat += '<h3>🤗 Support the project with a donation for the development of new features 🤗</h3>'
+        html_chat += '<br><a href="https://rebrand.ly/SupportAUTOGPTfree"><img src="https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif" alt="PayPal donate button" /></a>'
+        # add subheader with date and time
+        html_chat += '<br><br><h5>' + datetime.now().strftime("%d/%m/%Y %H:%M:%S") + \
+            '</h5></center><br><br>'
+        # add chat
+        # add solid container
+        html_chat += '<div style="padding: 10px; border-radius: 10px; margin: 10px; width: 100%; float: left;">'
+        for i in range(len(st.session_state['generated'])-1, -1, -1):
+            html_chat += '<div class="bot">' + \
+                st.session_state["generated"][i] + '</div><br>'
+            html_chat += '<div class="user">' + \
+                st.session_state['past'][i] + '</div><br>'
+        html_chat += '</div>'
+        # add footer
+        html_chat += '<br><br><center><small>Thanks you for using our ChatBOT 🧠🤖🇮🇹</small>'
+        # add link for danation
+        html_chat += '<h6>🤗 Support the project with a donation for the development of new features 🤗</h6>'
+        html_chat += '<br><a href="https://rebrand.ly/SupportAUTOGPTfree"><img src="https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif" alt="PayPal donate button" /></a><center>'
+
+        html_chat += '</body></html>'
+
+        # save file
+        # with open('chat.html', 'w') as f:
+        #     f.write(html_chat)
+
+        with open('chat.html', 'wb') as f:
+            f.write(html_chat.encode('utf-8'))
+        # download file
+        st.download_button(
+            label="📚 Download chat",
+            data=html_chat,
+            file_name='chat.html',
+            mime='text/html'
+        )

promptTemplate.py

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+"""
+This file contains the template for the prompt to be used for injecting the context into the model.
+
+With this technique we can use different plugin for different type of question and answer.
+Like :
+- Internet
+- Data
+- Code
+- PDF
+- Audio
+- Video
+
+"""
+
+from datetime import datetime
+now = datetime.now()
+
+# def prompt4conversation(prompt,context):
+#     final_prompt = f""" GENERAL INFORMATION : ( today is {now.strftime("%d/%m/%Y %H:%M:%S")} , You are a friendly helpful assistant.  
+#                         ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE , WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+#                         PREVIUS MESSAGE : ({context})
+#                         NOW THE USER ASK : {prompt} . 
+#                         WRITE THE ANSWER :"""
+#     return final_prompt
+
+def prompt4conversation(prompt,context):
+    final_prompt = f""" GENERAL INFORMATION : ( today is {now.strftime("%d/%m/%Y %H:%M:%S")} ,
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt} . 
+                        WRITE THE ANSWER :"""
+    return final_prompt
+
+def prompt4conversationInternet(prompt,context, internet, resume):
+    final_prompt = f""" GENERAL INFORMATION : ( today is {now.strftime("%d/%m/%Y %H:%M:%S")} ,
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}.
+                        INTERNET RESULT TO USE TO ANSWER : ({internet})
+                        INTERNET RESUME : ({resume})
+                        NOW THE USER ASK : {prompt}.
+                        WRITE THE FRIENDLY ANSWER BASED ON INTERNET INFORMATION :"""
+    return final_prompt
+
+def prompt4Data(prompt, context, solution):
+    final_prompt = f""" ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE , YOU MUST MAKE THE CORRECT ANSWER MORE ARGUMENTED ! IF THE CORRECT ANSWER CONTAINS CODE YOU ARE OBLIGED TO INSERT IT IN YOUR NEW ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CORRECT ANSWER : ({solution}) 
+                        MAKE THE ANSWER MORE ARGUMENTED, WITHOUT CHANGING ANYTHING OF THE CORRECT ANSWER :"""
+    return final_prompt
+
+def prompt4Code(prompt, context, solution):
+    final_prompt = f""" ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE , THE CORRECT ANSWER CONTAINS CODE YOU ARE OBLIGED TO INSERT IT IN YOUR NEW ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CODE FOR THE ANSWER : ({solution}) 
+                        WITHOUT CHANGING ANYTHING OF THE CODE of CORRECT ANSWER , MAKE THE ANSWER MORE DETALIED INCLUDING THE CORRECT CODE :"""
+    return final_prompt
+
+
+def prompt4Context(prompt, context, solution):
+    final_prompt = f""" ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE ,WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CORRECT ANSWER : ({solution}) 
+                        WITHOUT CHANGING ANYTHING OF CORRECT ANSWER , MAKE THE ANSWER MORE DETALIED:"""
+    return final_prompt
+
+
+def prompt4Audio(prompt, context, solution):
+    final_prompt = f""" ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE ,WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CORRECT ANSWER based on Audio text gived in input : ({solution}) 
+                        WITHOUT CHANGING ANYTHING OF CORRECT ANSWER , MAKE THE ANSWER MORE DETALIED:"""
+    return final_prompt
+
+def prompt4YT(prompt, context, solution):
+    final_prompt = f""" ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE ,WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CORRECT ANSWER based on Youtube video gived in input : ({solution}) 
+                        WITHOUT CHANGING ANYTHING OF CORRECT ANSWER , MAKE THE ANSWER MORE DETALIED:"""
+    return final_prompt
+
+
+#HOW TO ADD YOUR OWN PROMPT :
+# 1) ADD YOUR FUNCTION HERE, for example : def prompt4Me(prompt, context):
+# 2) WRITE THE PROMPT TEMPLATE FOR YOUR FUNCTION, for example : template = f"YOU IS : {context} , NOW THE USER ASK : {prompt} . WRITE THE ANSWER :"
+# 3) RETURN THE TEMPLATE, for example : return template
+# 4) IMPORT YOUR FUNCTION IN THE MAIN FILE (streamlit_app.py) , for example : from promptTemplate import prompt4Me
+# 5) FOLLOW OTHER SPTEP IN THE MAIN FILE (streamlit_app.py)

requirements.txt

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+beautifulsoup4==4.12.2
+docx2txt==0.8
+sketch==0.4.2
+hugchat
+langchain
+streamlit==1.24.0
+streamlit_extras==0.2.7
+pandas==2.0.1
+pdfplumber==0.9.0
+pydub==0.25.1
+requests
+urllib3
+duckduckgo_search
+SpeechRecognition==3.8.1
+youtube_search_python==1.6.6
+youtube_transcript_api==0.6.1
+chromadb==0.4.24
+ffmpeg-python
+ffprobe
+huggingface_hub