MyCommit 1

app.py

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+from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext, StorageContext, load_index_from_storage
+from langchain import OpenAI
+import gradio
+import os
+
+os.environ["OPENAI_API_KEY"] = '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'
+
+def construct_index(directory_path):
+    # set number of output tokens
+    num_outputs = 256
+
+    _llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+
+    service_context = ServiceContext.from_defaults(llm_predictor=_llm_predictor)
+
+    docs = SimpleDirectoryReader(directory_path).load_data()
+
+    index = GPTVectorStoreIndex.from_documents(docs, service_context=service_context)
+    
+    #Directory in which the indexes will be stored
+    index.storage_context.persist(persist_dir="indexes")
+
+    return index
+
+def chatbot(input_text):
+    
+    # rebuild storage context
+    storage_context = StorageContext.from_defaults(persist_dir="indexes")
+    
+    #load indexes from directory using storage_context 
+    query_engne = load_index_from_storage(storage_context).as_query_engine()
+    
+    response = query_engne.query(input_text)
+    
+    #returning the response
+    return response.response
+
+#Creating the web UIusing gradio
+iface = gradio.Interface(fn=chatbot,
+                     inputs=gradio.inputs.Textbox(lines=5, label="Enter your question here"),
+                     outputs="text",
+                     title="Custom-trained AI Chatbot")
+
+#Constructing indexes based on the documents in traininData folder
+#This can be skipped if you have already trained your app and need to re-run it
+index = construct_index("trainingData")
+
+#launching the web UI using gradio
+iface.launch(share=True)