1st commit!
Browse files- app.py +38 -0
- requirements.txt +2 -0
app.py
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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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openai.api_key = os.getenv('OPENAI_API_KEY')
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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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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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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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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()
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requirements.txt
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openai
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python-dotenv
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