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Update app.py
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app.py
CHANGED
@@ -5,12 +5,18 @@ import sys
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import os
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from IPython.display import Markdown, display
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max_input_size = 4096
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num_outputs = 2000
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max_chunk_overlap = 20
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chunk_size_limit = 600
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llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
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prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
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@@ -22,6 +28,9 @@ def train_with_data(directory_path):
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return index
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'''
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def ask_ai():
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index = GPTSimpleVectorIndex.load_from_disk('index.json')
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@@ -30,22 +39,20 @@ def ask_ai():
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response = index.query(query, response_mode="compact")
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display(Markdown(f"Response: <b>{response.response}</b>"))
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'''
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if
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os.environ["OPENAI_API_KEY"] = api_key
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index = GPTSimpleVectorIndex.load_from_disk('index.json')
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response = index.query(
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return response.response
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# Create Gradio interface to prompt for API key
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api_key = gr.inputs.Textbox(label="Enter your OpenAI API key
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# Define the interface
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iface = gr.Interface(fn=ask_ai, inputs=["text", api_key], outputs="text" ,title="Jim's Chatbot")
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import os
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from IPython.display import Markdown, display
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def construct_index(directory_path):
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# set maximum input size
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max_input_size = 4096
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# set number of output tokens
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num_outputs = 2000
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# set maximum chunk overlap
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max_chunk_overlap = 20
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# set chunk size limit
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chunk_size_limit = 600
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# define LLM
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llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
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prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
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return index
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'''
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def ask_ai():
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index = GPTSimpleVectorIndex.load_from_disk('index.json')
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response = index.query(query, response_mode="compact")
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display(Markdown(f"Response: <b>{response.response}</b>"))
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'''
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# Define the ask_ai() function
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def ask_ai(question,api):
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if api == "":
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api = "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"
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os.environ["OPENAI_API_KEY"] = api
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index = GPTSimpleVectorIndex.load_from_disk('index.json')
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response = index.query(question, response_mode="compact")
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return response.response
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construct_index("context_data")
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# Create Gradio interface to prompt for API key
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api_key = gr.inputs.Textbox(label="Enter your OpenAI API key:")
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# Define the interface
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iface = gr.Interface(fn=ask_ai, inputs=["text", api_key], outputs="text" ,title="Jim's Chatbot")
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