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  1. app.py +38 -0
  2. requirements.txt +2 -0
app.py ADDED
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+ import openai
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+ import os
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+ import gradio as gr
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+ import json
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+ from dotenv import load_dotenv, find_dotenv
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+ _ = load_dotenv(find_dotenv())
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+
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+
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+ openai.api_key = os.getenv('OPENAI_API_KEY')
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+
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+ def get_completion_from_messages(messages,
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+ model="gpt-3.5-turbo",
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+ temperature=0,
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+ max_tokens=500):
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+ response = openai.ChatCompletion.create(
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+ model=model,
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+ messages=messages,
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+ temperature=temperature, # this is the degree of randomness of the model's output
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+ max_tokens=max_tokens, # the maximum number of tokens the model can ouptut
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+ )
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+ return response.choices[0].message["content"]
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+
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+ def greet(company, solution, target_customer, problem, features, customer_persona="the target customer"):
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+ pitch = f"""My company, {company} is developing {solution} to help {target_customer} {problem} with {features}"""
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+ sys_setup = f"""
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+ Determine the product or solution, the problem being solved, features, target customer that are being discussed in the \
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+ following user prompt. State if you would use this product and elaborate on why. Also state if you would pay for it and elaborate on why.\
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+ Give a score for the product.
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+
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+ Format your response as a JSON object with \
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+ 'solution', 'problem', 'features', 'target_customer', 'fg_will_use', 'reason_to_use', 'fg_will_pay', 'reason_to_pay', 'score' as the keys.
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+ """
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+ messages = [{'role':'system', 'content':"You are " + customer_persona + "."}, {'role':'system', 'content': sys_setup}, {'role':'user','content':pitch}]
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+ response = get_completion_from_messages(messages, temperature=0)
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+ return json.dumps(response)
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+
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+ iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Company"), gr.Textbox(label="Solution"), gr.Textbox(label="Customer"), gr.Textbox(label="Problem"), gr.Textbox(label="Feature"), gr.Textbox(label="Customer persona", lines=3)], outputs="json")
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+ iface.launch()
requirements.txt ADDED
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+ openai
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+ python-dotenv