Create app.py

app.py

@@ -0,0 +1,45 @@
+from llama_index import SimpleDirectoryReader, GPTListIndex, GPTVectorStoreIndex, StorageContext, LLMPredictor, PromptHelper, load_index_from_storage
+from langchain.chat_models import ChatOpenAI
+import gradio as gr
+import sys
+import os
+
+os.environ["OPENAI_API_KEY"] = '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'
+
+def construct_index(directory_path):
+    max_input_size = 1000000
+    num_outputs = 256
+    max_chunk_overlap = 20
+    chunk_size_limit = 2048
+
+    prompt_helper = PromptHelper(max_input_size, num_outputs, chunk_overlap_ratio= 0.1, chunk_size_limit=chunk_size_limit)
+
+    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=1, model_name="gpt-4", max_tokens=num_outputs))
+
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    index = GPTVectorStoreIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper,chunk_size_limit = 2048)
+
+    
+    index.storage_context.persist(persist_dir="index.json")
+
+    return index
+
+def chatbot(input_text):
+
+    storage_context = StorageContext.from_defaults(persist_dir="index.json")
+    index = load_index_from_storage(storage_context)
+
+    query_engine = index.as_query_engine()
+    response = query_engine.query(input_text)
+
+    
+    return response.response
+
+iface = gr.Interface(fn=chatbot,
+                     inputs=gr.components.Textbox(lines=7, label="Enter your text"),
+                     outputs="text",
+                     title="Custom-trained AI Chatbot")
+
+index = construct_index("docs")
+iface.launch()