Update app.py

app.py

@@ -45,7 +45,6 @@ def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
         temp3["role"] = "user" 
         temp3["content"] = inputs
         messages.append(temp3)
-        #messages
         payload = {
         "model": "gpt-3.5-turbo",
         "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
@@ -61,28 +60,19 @@ def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
     # 4. POST it to OPENAI API
     history.append(inputs)
     print(f"payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
     response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    #response = requests.post(API_URL, headers=headers, json=payload, stream=True)
     token_counter = 0 
     partial_words = "" 
 
     # 5. Iterate through response lines and structure readable response
-    # TODO - make this parse out markdown so we can have similar interface
     counter=0
     for chunk in response.iter_lines():
-        #Skipping first chunk
         if counter == 0:
           counter+=1
           continue
-        #counter+=1
-        # check whether each line is non-empty
         if chunk.decode() :
           chunk = chunk.decode()
-          # decode each line as response data is in bytes
           if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-              #  break
               partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
               if token_counter == 0:
                 history.append(" " + partial_words)
@@ -90,15 +80,72 @@ def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
                 history[-1] = partial_words
               chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
               token_counter+=1
-              yield chat, history, chat_counter  # resembles {chatbot: chat, state: history}  
+              yield chat, history, chat_counter 
                    
 
 def reset_textbox():
     return gr.update(value='')
 
+
+    
+
+# Episodic and Semantic IO
+def list_files(file_path):
+    import os
+    icon_csv = "📄 "
+    icon_txt = "📑 "
+    current_directory = os.getcwd()
+    file_list = []
+    for filename in os.listdir(current_directory):
+        if filename.endswith(".csv"):
+            file_list.append(icon_csv + filename)
+        elif filename.endswith(".txt"):
+            file_list.append(icon_txt + filename)
+    if file_list:
+        return "\n".join(file_list)
+    else:
+        return "No .csv or .txt files found in the current directory."
+
+# Function to read a file
+def read_file(file_path):
+    try:
+        with open(file_path, "r") as file:
+            contents = file.read()
+            return f"{contents}"
+            #return f"Contents of {file_path}:\n{contents}"
+    except FileNotFoundError:
+        return "File not found."
+
+# Function to delete a file
+def delete_file(file_path):
+    try:
+        import os
+        os.remove(file_path)
+        return f"{file_path} has been deleted."
+    except FileNotFoundError:
+        return "File not found."
+
+# Function to write to a file
+def write_file(file_path, content):
+    try:
+        with open(file_path, "w") as file:
+            file.write(content)
+        return f"Successfully written to {file_path}."
+    except:
+        return "Error occurred while writing to file."
+
+# Function to append to a file
+def append_file(file_path, content):
+    try:
+        with open(file_path, "a") as file:
+            file.write(content)
+        return f"Successfully appended to {file_path}."
+    except:
+        return "Error occurred while appending to file."
+
+
 title = """<h1 align="center">Memory Chat Story Generator ChatGPT</h1>"""
 description = """
-
 ## ChatGPT Datasets 📚
 - WebText
 - Common Crawl
@@ -106,7 +153,6 @@ description = """
 - English Wikipedia
 - Toronto Books Corpus
 - OpenWebText
-
 ## ChatGPT Datasets - Details 📚
 - **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2.
   - [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext)
@@ -119,33 +165,45 @@ description = """
 - **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto.
   - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze.
 - **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3.
-  - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al.
-  
+  - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al.  
   """
 
 # 6. Use Gradio to pull it all together
-with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
-                #chatbot {height: 520px; overflow: auto;}""") as demo:
-
-                    
+with gr.Blocks(css = """#col_container {width: 1400px; margin-left: auto; margin-right: auto;} #chatbot {height: 600px; overflow: auto;}""") as demo:
     gr.HTML(title)
-
-                    
     with gr.Column(elem_id = "col_container"):
-        chatbot = gr.Chatbot(elem_id='chatbot') #c
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
-        state = gr.State([]) #s
+        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
+        chatbot = gr.Chatbot(elem_id='chatbot')
+        state = gr.State([])
         b1 = gr.Button()
-    
         with gr.Accordion("Parameters", open=False):
             top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
             temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
+            chat_counter = gr.Number(value=0, visible=True, precision=0)
+
+            
+    # Episodic/Semantic IO
+    fileName = gr.Textbox(label="Filename")
+    fileContent = gr.TextArea(label="File Content")
+    completedMessage = gr.Textbox(label="Completed")
+    label = gr.Label()
+    with gr.Row():
+        listFiles = gr.Button("📄 List File(s)")
+        readFile = gr.Button("📖 Read File")
+        saveFile = gr.Button("💾 Save File")
+        deleteFile = gr.Button("🗑️ Delete File")
+        appendFile = gr.Button("➕ Append File")
+    listFiles.click(list_files, inputs=fileName, outputs=fileContent)
+    readFile.click(read_file, inputs=fileName, outputs=fileContent)
+    saveFile.click(write_file, inputs=[fileName, fileContent], outputs=completedMessage)
+    deleteFile.click(delete_file, inputs=fileName, outputs=completedMessage)
+    appendFile.click(append_file, inputs=[fileName, fileContent], outputs=completedMessage )
 
-    inputs.submit( predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter],)
-    b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
+    
+    inputs.submit(predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter])
+    b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter])
     b1.click(reset_textbox, [], [inputs])
     inputs.submit(reset_textbox, [], [inputs])
-                    
     gr.Markdown(description)
-    demo.queue().launch(debug=True)
+    
+    demo.queue().launch(debug=True)