Update app.py

app.py

@@ -1,62 +1,62 @@
-from flask import Flask, jsonify, request, send_file
-from gtts import gTTS
-from langchain.chains import ConversationalRetrievalChain
-from langchain.chat_models import ChatOpenAI
-from langchain.document_loaders import TextLoader
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.indexes import VectorstoreIndexCreator
-from langchain.indexes.vectorstore import VectorStoreIndexWrapper
-from langchain.vectorstores import Chroma
-from langchain.memory import ConversationBufferMemory
-import os
-
-app = Flask(__name__)
-os.environ["OPENAI_API_KEY"] = "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"
-PERSIST = True
-
-query = None  
-
-def main_func(message, history):
-    global query  
-    chat_history = history  
-    if PERSIST and os.path.exists("persist"):
-        print("Reusing index...\n")
-        vectorstore = Chroma(persist_directory="persist", embedding_function=OpenAIEmbeddings())
-        index = VectorStoreIndexWrapper(vectorstore=vectorstore)
-    else:
-        loader = TextLoader("new.txt")
-        if PERSIST:
-            index = VectorstoreIndexCreator(vectorstore_kwargs={"persist_directory": "persist"}).from_loaders([loader])
-        else:
-            index = VectorstoreIndexCreator().from_loaders([loader])
-
-    print(index)
-
-    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-    chain = ConversationalRetrievalChain.from_llm(llm=ChatOpenAI(), retriever=index.vectorstore.as_retriever(), memory=memory, verbose=True)
-
-    query = message
-    result = chain({"question": query, "chat_history": chat_history})
-    print(result['answer'])
-    chat_history.append((query, result['answer']))
-    return result['answer']
-
-@app.route('/generate-text/<input_text>', methods=['POST'])
-def generate_text(input_text):
-    global query  
-    generated_text = main_func(input_text, [])
-
-    tts = gTTS(text=generated_text, lang='en')
-    tts.save("output.mp3")
-
-    return jsonify({
-        'generated_text': generated_text,
-        'audio_url': request.host_url + 'audio'
-    })
-
-@app.route('/audio')
-def get_audio():
-    return send_file("output.mp3", as_attachment=True)
-
-if __name__ == "__main__":
-    app.run(debug=True)
+from flask import Flask, jsonify, request, send_file
+from gtts import gTTS
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import TextLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.indexes.vectorstore import VectorStoreIndexWrapper
+from langchain.vectorstores import Chroma
+from langchain.memory import ConversationBufferMemory
+import os
+
+app = Flask(__name__)
+os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
+PERSIST = True
+
+query = None  
+
+def main_func(message, history):
+    global query  
+    chat_history = history  
+    if PERSIST and os.path.exists("persist"):
+        print("Reusing index...\n")
+        vectorstore = Chroma(persist_directory="persist", embedding_function=OpenAIEmbeddings())
+        index = VectorStoreIndexWrapper(vectorstore=vectorstore)
+    else:
+        loader = TextLoader("new.txt")
+        if PERSIST:
+            index = VectorstoreIndexCreator(vectorstore_kwargs={"persist_directory": "persist"}).from_loaders([loader])
+        else:
+            index = VectorstoreIndexCreator().from_loaders([loader])
+
+    print(index)
+
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    chain = ConversationalRetrievalChain.from_llm(llm=ChatOpenAI(), retriever=index.vectorstore.as_retriever(), memory=memory, verbose=True)
+
+    query = message
+    result = chain({"question": query, "chat_history": chat_history})
+    print(result['answer'])
+    chat_history.append((query, result['answer']))
+    return result['answer']
+
+@app.route('/generate-text/<input_text>', methods=['POST'])
+def generate_text(input_text):
+    global query  
+    generated_text = main_func(input_text, [])
+
+    tts = gTTS(text=generated_text, lang='en')
+    tts.save("output.mp3")
+
+    return jsonify({
+        'generated_text': generated_text,
+        'audio_url': request.host_url + 'audio'
+    })
+
+@app.route('/audio')
+def get_audio():
+    return send_file("output.mp3", as_attachment=True)
+
+if __name__ == "__main__":
+    app.run(debug=True)