Update app.py

app.py

@@ -1,56 +1,45 @@
-import gradio as gr
 import openai
-import time
-import numpy as np
-from sklearn.feature_extraction.text import CountVectorizer
-
-# The cache dictionary stores the responses to previous requests
-cache = {}
-chat_history = []
-
-def get_similarity(message1, message2):
-    # Convert the messages to arrays of word vectors
-    vectorizer = CountVectorizer().fit_transform([message1, message2])
-    vectors = vectorizer.toarray()
-    # Calculate the cosine similarity between the vectors
-    similarity = np.dot(vectors[0], vectors[1]) / (np.linalg.norm(vectors[0]) * np.linalg.norm(vectors[1]))
-    # Return the similarity score
-    return similarity
+import gradio as gr
 
-def chatbot(prompt, api_key):
-    global cache, chat_history
-    similarity_threshold = 0.8 # Set the similarity threshold for repeated requests
-    # Compare the current message to the previous messages and see if they are similar enough to be considered a repeated request
-    for message in chat_history:
-        similarity = get_similarity(prompt, message)
-        if similarity >= similarity_threshold:
-            return "You already asked me that!"
-    # If the prompt is not a repeated request, proceed with the chatbot logic
-    openai.api_key = api_key
+# Define the chat function
+def chat(api_key, message):
+    # Set up the OpenAI API request
     response = openai.Completion.create(
         engine="davinci",
-        prompt=prompt,
-        temperature=0.7,
+        prompt=message,
         max_tokens=1024,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0
+        n=1,
+        stop=None,
+        temperature=0.5,
+        api_key=api_key,
     )
-    message = response.choices[0].text.strip()
-    # Add the response to the cache and chat history
-    cache[prompt] = message
-    chat_history.append(prompt)
-    # Wait for a short time to avoid hitting the rate limit of the OpenAI API
-    time.sleep(0.1)
-    return message
+    
+    # Extract the bot's response from the API request
+    bot_response = response.choices[0].text.strip()
+    
+    return bot_response
 
-def chat_interface():
-    input_api_key = gr.inputs.Textbox(label="Enter your OpenAI API key")
-    input_text = gr.inputs.Textbox(lines=7, label="Enter your message")
-    output_text = gr.outputs.Textbox(label="Bot's reply")
-    chat_button = gr.Interface(fn=chatbot, inputs=[input_text, input_api_key], outputs=output_text, title="OpenAI Chatbot", description="An AI chatbot powered by OpenAI's GPT-3 language model.", examples=[["hello", "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"]], allow_flagging=False, layout="vertical", live=False, theme="huggingface", inputs_inline=True, outputs_inline=True)
+# Define the Gradio interface
+api_key_input = gr.inputs.Textbox(label="OpenAI API Key", default=None)
+message_input = gr.inputs.Textbox(label="Enter your message here")
+output = gr.outputs.Textbox(label="Bot response")
 
-    return chat_button
+chat_button = gr.Interface(
+    fn=chat,
+    inputs=[api_key_input, message_input],
+    outputs=output,
+    title="OpenAI Chatbot",
+    description="Enter your message below to chat with an AI",
+    theme="compact",
+    layout="vertical",
+    allow_flagging=False,
+    allow_screenshot=False,
+    allow_share=False,
+)
 
-chat_button = chat_interface()
-chat_button.launch()
+# Check if an API key has been provided
+if api_key_input.value is None:
+    print("Please enter your OpenAI API key and try again.")
+else:
+    # Launch the app
+    chat_button.launch()