First implementation of streamlit chat app in gui folder

.gitignore

@@ -7,6 +7,15 @@
 /dataSources/
 /dataSources.local.xml
 
+# Ignore local python virtual environment
+venv/
+
+# Ignore streamlit_chat_app.py conversations pickle
+conversations.pkl
+
+# Ignore accounts created by api's
+accounts.txt
+
 .idea/
 
 */__pycache__/

gui/README.md

@@ -1,11 +1,72 @@
 # gpt4free gui
 
-mode `streamlit_app.py` into base folder to run
+This code provides a Graphical User Interface (GUI) for gpt4free. Users can ask questions and get answers from GPT-4 API's, utilizing multiple API implementations. The project contains two different Streamlit applications: `streamlit_app.py` and `streamlit_chat_app.py`.
 
+Installation
+------------
+
+1.  Clone the repository.
+2.  Install the required dependencies with: `pip install -r requirements.txt`.
+3.  To use `streamlit_chat_app.py`, note that it depends on a pull request (PR #24) from the https://github.com/AI-Yash/st-chat/ repository, which may change in the future. The current dependency library can be found at https://github.com/AI-Yash/st-chat/archive/refs/pull/24/head.zip.
+
+Usage
+-----
+
+Choose one of the Streamlit applications to run:
+
+### streamlit\_app.py
+
+This application provides a simple interface for asking GPT-4 questions and receiving answers.
+
+To run the application:
+
+run:
+```arduino
+streamlit run gui/streamlit_app.py
+```
+<br>
+
+<img width="724" alt="image" src="https://user-images.githubusercontent.com/98614666/234232449-0d5cd092-a29d-4759-8197-e00ba712cb1a.png">   
+
+<br>
+<br>
 
 preview:  
+
 <img width="1125" alt="image" src="https://user-images.githubusercontent.com/98614666/234232398-09e9d3c5-08e6-4b8a-b4f2-0666e9790c7d.png">    
 
 
-run:    
+### streamlit\_chat\_app.py
-<img width="724" alt="image" src="https://user-images.githubusercontent.com/98614666/234232449-0d5cd092-a29d-4759-8197-e00ba712cb1a.png">   
+
+This application provides a chat-like interface for asking GPT-4 questions and receiving answers. It supports multiple query methods, and users can select the desired API for their queries. The application also maintains a conversation history.
+
+To run the application:
+
+```arduino
+streamlit run streamlit_chat_app.py
+```
+
+<br>
+
+<img width="724" alt="image" src="image1.png">   
+
+<br>
+<br>
+
+preview:  
+
+<img width="1125" alt="image" src="image2.png">    
+
+Contributing
+------------
+
+Feel free to submit pull requests, report bugs, or request new features by opening issues on the GitHub repository.
+
+Bug
+----
+There is a bug in `streamlit_chat_app.py` right now that I haven't pinpointed yet, probably is really simple but havent had the time to look for it. Whenever you open a new conversation or access an old conversation it will only start prompt-answering after the second time you input to the text input, other than that, everything else seems to work accordingly.
+
+License
+-------
+
+This project is licensed under the MIT License.

gui/query_methods.py

@@ -0,0 +1,163 @@
+import forefront, quora, theb, you
+import random
+
+
+
+def query_forefront(question: str) -> str:
+    # create an account
+    token = forefront.Account.create(logging=True)
+    
+    # get a response
+    try:
+        result = forefront.StreamingCompletion.create(token = token, prompt = 'hello world', model='gpt-4')
+        
+        return result['response']
+    
+    except Exception as e:
+        # Return error message if an exception occurs
+        return f'An error occurred: {e}. Please make sure you are using a valid cloudflare clearance token and user agent.'
+
+
+def query_quora(question: str) -> str:
+    token = quora.Account.create(logging=False, enable_bot_creation=True)
+    response = quora.Completion.create(
+        model='gpt-4',
+        prompt=question,
+        token=token
+    )
+
+    return response.completion.choices[0].tex
+
+
+def query_theb(question: str) -> str:
+    # Set cloudflare clearance cookie and get answer from GPT-4 model
+    try:
+        result = theb.Completion.create(
+            prompt = question)
+        
+        return result['response']
+    
+    except Exception as e:
+        # Return error message if an exception occurs
+        return f'An error occurred: {e}. Please make sure you are using a valid cloudflare clearance token and user agent.'
+
+
+def query_you(question: str) -> str:
+    # Set cloudflare clearance cookie and get answer from GPT-4 model
+    try:
+        result = you.Completion.create(
+            prompt = question)
+        
+        return result['response']
+    
+    except Exception as e:
+        # Return error message if an exception occurs
+        return f'An error occurred: {e}. Please make sure you are using a valid cloudflare clearance token and user agent.'
+
+# Define a dictionary containing all query methods
+avail_query_methods = {
+    "Forefront": query_forefront,
+    "Quora": query_quora,
+    "Theb": query_theb,
+    "You": query_you,
+    # "Writesonic": query_writesonic,
+    # "T3nsor": query_t3nsor,
+    # "Phind": query_phind,
+    # "Ora": query_ora,
+}
+
+def query(user_input: str, selected_method: str = "Random") -> str:
+
+    # If a specific query method is selected (not "Random") and the method is in the dictionary, try to call it
+    if selected_method != "Random" and selected_method in avail_query_methods:
+        try:
+            return avail_query_methods[selected_method](user_input)
+        except Exception as e:
+            print(f"Error with {selected_method}: {e}")
+            return "😵 Sorry, some error occurred please try again."
+
+    # Initialize variables for determining success and storing the result
+    success = False
+    result = "😵 Sorry, some error occurred please try again."
+    # Create a list of available query methods
+    query_methods_list = list(avail_query_methods.values())
+
+    # Continue trying different methods until a successful result is obtained or all methods have been tried
+    while not success and query_methods_list:
+        # Choose a random method from the list
+        chosen_query = random.choice(query_methods_list)
+        # Find the name of the chosen method
+        chosen_query_name = [k for k, v in avail_query_methods.items() if v == chosen_query][0]
+        try:
+            # Try to call the chosen method with the user input
+            result = chosen_query(user_input)
+            success = True
+        except Exception as e:
+            print(f"Error with {chosen_query_name}: {e}")
+            # Remove the failed method from the list of available methods
+            query_methods_list.remove(chosen_query)
+
+    return result
+
+
+__all__ = ['query', 'avail_query_methods']
+
+
+
+# def query_ora(question:str)->str:
+#     result =""
+#     try:
+#         gpt4_chatbot_ids = ['b8b12eaa-5d47-44d3-92a6-4d706f2bcacf', 'fbe53266-673c-4b70-9d2d-d247785ccd91', 'bd5781cf-727a-45e9-80fd-a3cfce1350c6', '993a0102-d397-47f6-98c3-2587f2c9ec3a', 'ae5c524e-d025-478b-ad46-8843a5745261', 'cc510743-e4ab-485e-9191-76960ecb6040', 'a5cd2481-8e24-4938-aa25-8e26d6233390', '6bca5930-2aa1-4bf4-96a7-bea4d32dcdac', '884a5f2b-47a2-47a5-9e0f-851bbe76b57c', 'd5f3c491-0e74-4ef7-bdca-b7d27c59e6b3', 'd72e83f6-ef4e-4702-844f-cf4bd432eef7', '6e80b170-11ed-4f1a-b992-fd04d7a9e78c', '8ef52d68-1b01-466f-bfbf-f25c13ff4a72', 'd0674e11-f22e-406b-98bc-c1ba8564f749', 'a051381d-6530-463f-be68-020afddf6a8f', '99c0afa1-9e32-4566-8909-f4ef9ac06226', '1be65282-9c59-4a96-99f8-d225059d9001', 'dba16bd8-5785-4248-a8e9-b5d1ecbfdd60', '1731450d-3226-42d0-b41c-4129fe009524', '8e74635d-000e-4819-ab2c-4e986b7a0f48', 'afe7ed01-c1ac-4129-9c71-2ca7f3800b30', 'e374c37a-8c44-4f0e-9e9f-1ad4609f24f5']
+#         chatbot_id = random.choice(gpt4_chatbot_ids)
+#         model = ora.CompletionModel.load(chatbot_id, 'gpt-4')
+#         response = ora.Completion.create(model, question)
+#         result = response.completion.choices[0].text
+#     except  Exception as e:
+#         print(f"Error : {e}")
+#         result = "😵 Sorry, some error occurred please try again."
+#     return result
+
+
+# def query_writesonic(question:str)->str:
+#     account = writesonic.Account.create(logging = False)
+#     response = writesonic.Completion.create(
+#         api_key = account.key,
+#         prompt  = question,
+#     )
+    
+#     return response.completion.choices[0].text
+
+
+# def query_t3nsor(question: str) -> str:
+#     messages = []
+    
+#     user = question
+
+#     t3nsor_cmpl = t3nsor.Completion.create(
+#         prompt=user,
+#         messages=messages
+#     )
+
+#     messages.extend([
+#         {'role': 'user', 'content': user},
+#         {'role': 'assistant', 'content': t3nsor_cmpl.completion.choices[0].text}
+#     ])
+
+#     return t3nsor_cmpl.completion.choices[0].text
+
+
+
+# def query_phind(question:str)->str:
+#     phind.cf_clearance = 'KvXc1rh.TFQG1rNF0eMlcpJbsdmJkYgvmqS42OOfqUk-1682393898-0-160'
+#     # phind.cf_clearance = 'heguhSRBB9d0sjLvGbQECS8b80m2BQ31xEmk9ChshKI-1682268995-0-160'
+#     # phind.user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36'
+#     phind.user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.4.1 Safari/605.1.15'
+#     result = phind.Completion.create(
+#     model  = 'gpt-4',
+#     prompt = question,
+#     results     = phind.Search.create(question, actualSearch = False),
+#     creative    = False,
+#     detailed    = False,
+#     codeContext = '') 
+#     # print(result.completion.choices[0].text)
+#     return result.completion.choices[0].text

gui/streamlit_chat_app.py

@@ -0,0 +1,97 @@
+import os
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
+
+import streamlit as st
+from streamlit_chat import message
+from query_methods import query, avail_query_methods
+import pickle
+
+
+conversations_file = "conversations.pkl"
+
+def load_conversations():
+    try:
+        with open(conversations_file, "rb") as f:
+            return pickle.load(f)
+    except FileNotFoundError:
+        return []
+
+def save_conversations(conversations, current_conversation):
+    updated = False
+    for i, conversation in enumerate(conversations):
+        if conversation == current_conversation:
+            conversations[i] = current_conversation
+            updated = True
+            break
+    if not updated:
+        conversations.append(current_conversation)
+    with open(conversations_file, "wb") as f:
+        pickle.dump(conversations, f)
+
+st.header("Chat Placeholder")
+
+if 'conversations' not in st.session_state:
+    st.session_state['conversations'] = load_conversations()
+
+if 'input_text' not in st.session_state:
+    st.session_state['input_text'] = ''
+
+if 'selected_conversation' not in st.session_state:
+    st.session_state['selected_conversation'] = None
+
+if 'input_field_key' not in st.session_state:
+    st.session_state['input_field_key'] = 0
+    
+if 'query_method' not in st.session_state:
+    st.session_state['query_method'] = query
+
+# Initialize new conversation
+if 'current_conversation' not in st.session_state or st.session_state['current_conversation'] is None:
+    st.session_state['current_conversation'] = {'user_inputs': [], 'generated_responses': []}
+
+
+input_placeholder = st.empty()
+user_input = input_placeholder.text_input('You:', key=f'input_text_{len(st.session_state["current_conversation"]["user_inputs"])}')
+submit_button = st.button("Submit")
+
+if user_input or submit_button:
+    output = query(user_input, st.session_state['query_method'])
+    
+    st.session_state.current_conversation['user_inputs'].append(user_input)
+    st.session_state.current_conversation['generated_responses'].append(output)
+    save_conversations(st.session_state.conversations, st.session_state.current_conversation)
+    user_input = input_placeholder.text_input('You:', value='', key=f'input_text_{len(st.session_state["current_conversation"]["user_inputs"])}')  # Clear the input field
+
+
+# Add a button to create a new conversation
+if st.sidebar.button("New Conversation"):
+    st.session_state['selected_conversation'] = None
+    st.session_state['current_conversation'] = {'user_inputs': [], 'generated_responses': []}
+    st.session_state['input_field_key'] += 1
+
+
+st.session_state['query_method'] = st.sidebar.selectbox(
+    "Select API:",
+    options=avail_query_methods.keys(),
+    index=0
+)
+
+# Sidebar
+st.sidebar.header("Conversation History")
+
+for i, conversation in enumerate(st.session_state.conversations):
+    if st.sidebar.button(f"Conversation {i + 1}: {conversation['user_inputs'][0]}", key=f"sidebar_btn_{i}"):
+        st.session_state['selected_conversation'] = i
+        st.session_state['current_conversation'] = st.session_state.conversations[i]
+
+if st.session_state['selected_conversation'] is not None:
+    conversation_to_display = st.session_state.conversations[st.session_state['selected_conversation']]
+else:
+    conversation_to_display = st.session_state.current_conversation
+
+if conversation_to_display['generated_responses']:
+    for i in range(len(conversation_to_display['generated_responses']) - 1, -1, -1):
+        message(conversation_to_display["generated_responses"][i], key=f"display_generated_{i}")
+        message(conversation_to_display['user_inputs'][i], is_user=True, key=f"display_user_{i}")

requirements.txt

@@ -9,3 +9,4 @@ streamlit==1.21.0
 selenium
 fake-useragent
 twocaptcha
+https://github.com/AI-Yash/st-chat/archive/refs/pull/24/head.zip