Update app.py

app.py

@@ -1,37 +1,50 @@
 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
 
 def chatbot(prompt, api_key):
-    if prompt in cache:
-        # If the prompt is already in the cache, return the cached response
-        return cache[prompt]
-    else:
-        # If the prompt is not in the cache, make a request to the OpenAI API
-        openai.api_key = api_key
-        response = openai.Completion.create(
-            engine="davinci",
-            prompt=prompt,
-            temperature=0.7,
-            max_tokens=1024,
-            top_p=1,
-            frequency_penalty=0,
-            presence_penalty=0
-        )
-        message = response.choices[0].text.strip()
-        # Add the response to the cache
-        cache[prompt] = message
-        # Wait for a short time to avoid hitting the rate limit of the OpenAI API
-        time.sleep(0.1)
-        return message
+    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
+    response = openai.Completion.create(
+        engine="davinci",
+        prompt=prompt,
+        temperature=0.7,
+        max_tokens=1024,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0
+    )
+    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
 
 def chat_interface():
-    chat_history = []
-    chat_history.append("Bot: Hi there! How can I assist you today?")
-
     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")