Upload app.py
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app.py
CHANGED
@@ -105,19 +105,24 @@ def predict(user_input,company):
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}
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# Create messages
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response = client.chat.completions.create(
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model=model_name,
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messages=prompt,
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temperature=0
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)
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# Get response from the LLM
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prediction = response.choices[0].message.content.strip()
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# While the prediction is made, log both the inputs and outputs to a local log file
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# While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
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# access
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@@ -142,7 +147,7 @@ company_input = gr.Radio(
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label = 'company'
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)
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model_output = gr.
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# Add text box and radio button to the interface
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# The radio button is used to select the company 10k report in which the context needs to be retrieved.
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@@ -155,7 +160,7 @@ model_output = gr.Textbox (label = 'Response')
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demo = gr.Interface(
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fn=predict,
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inputs=[user_input,company_input],
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outputs=
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title="RAG on 10k-reports",
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description="This API allows you to query on annaul reports",
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concurrency_limit=16
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)
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}
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]
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# Create messages
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try:
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response = client.chat.completions.create(
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model=model_name,
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messages=prompt,
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temperature=0
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)
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prediction = response.choices[0].message.content.strip()
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except Exception as e:
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prediction = f'Sorry, I encountered the following error: \n {e}'
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# Get response from the LLM
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prediction = response.choices[0].message.content.strip()
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# While the prediction is made, log both the inputs and outputs to a local log file
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# While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
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# access
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label = 'company'
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)
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model_output = gr.Label (label = 'Response')
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# Add text box and radio button to the interface
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# The radio button is used to select the company 10k report in which the context needs to be retrieved.
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demo = gr.Interface(
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fn=predict,
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inputs=[user_input,company_input],
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outputs=prediction,
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title="RAG on 10k-reports",
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description="This API allows you to query on annaul reports",
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concurrency_limit=16
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