Create app.py

app.py

@@ -0,0 +1,163 @@
+import gradio as gr
+import os
+import time
+import openai
+import pandas as pd
+
+openai_api_key_textbox = ""
+model = None
+tokenizer = None
+generator = None
+csv_name = "disease_database_mini.csv"
+df = pd.read_csv(csv_name)
+openai.api_key = "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"
+
+def csv_prompter(question,csv_name):
+    
+    
+
+    fulltext = "A question is provided below. Given the question, extract " + \
+               "keywords from the text. Focus on extracting the keywords that we can use " + \
+               "to best lookup answers to the question. \n" + \
+               "---------------------\n" + \
+               "{}\n".format(question) + \
+               "---------------------\n" + \
+               "Provide keywords in the following comma-separated format.\nKeywords: "
+
+    messages = [
+        {"role": "system", "content": ""},
+    ]
+    messages.append(
+        {"role": "user", "content": f"{fulltext}"}
+    )
+    rsp = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=messages
+    )
+    keyword_list = rsp.get("choices")[0]["message"]["content"]
+    keyword_list = keyword_list.replace(",","").split(" ")
+
+    print(keyword_list)
+    divided_text = []
+    csvdata = df.to_dict('records')
+    step_length = 15
+    for csv_item in range(0,len(csvdata),step_length):
+        csv_text = str(csvdata[csv_item:csv_item+step_length]).replace("}, {", "\n\n").replace("\"", "")#.replace("[", "").replace("]", "")
+        divided_text.append(csv_text)
+
+    answer_llm = ""
+
+    score_textlist = [0] * len(divided_text)
+
+    for i, chunk in enumerate(divided_text):
+        for t, keyw in enumerate(keyword_list):
+            if keyw.lower() in chunk.lower():
+                score_textlist[i] = score_textlist[i] + 1
+
+    answer_list = []
+    divided_text = [item for _, item in sorted(zip(score_textlist, divided_text), reverse=True)]
+
+    for i, chunk in enumerate(divided_text):
+
+        if i>4:
+            continue
+
+        fulltext = "{}".format(chunk) + \
+                   "\n---------------------\n" + \
+                   "Based on the Table above and not prior knowledge, " + \
+                   "Select the Table Entries that will help to answer the question: {}\n Output in the format of \" Disease: <>; Symptom: <>; Medical Test: <>; Medications: <>;\". If there is no useful form entries, output: 'No Entry'".format(question)
+
+        print(fulltext)
+        messages = [
+            {"role": "system", "content": ""},
+        ]
+        messages.append(
+            {"role": "user", "content": f"{fulltext}"}
+        )
+        rsp = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=messages
+        )
+        answer_llm = rsp.get("choices")[0]["message"]["content"]
+
+        print("\nAnswer: " + answer_llm)
+        print()
+        if not "No Entry" in answer_llm:
+            answer_list.append(answer_llm)
+
+
+
+    fulltext = "The original question is as follows: {}\n".format(question) + \
+               "Based on this Table:\n" + \
+               "------------\n" + \
+               "{}\n".format(str("\n\n".join(answer_list))) + \
+               "------------\n" + \
+               "Answer: "
+    print(fulltext)
+    messages = [
+        {"role": "system", "content": ""},
+    ]
+    messages.append(
+        {"role": "user", "content": f"{fulltext}"}
+    )
+    rsp = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=messages
+    )
+    answer_llm = rsp.get("choices")[0]["message"]["content"]
+
+    print("\nFinal Answer: " + answer_llm)
+    print()
+
+    return answer_llm
+
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Autonomous ChatDoctor (openai version), based on disease database knowledge")
+    gr.Markdown("## Example: If I have frontal headache, fever, and painful sinuses, what disease should I have, and what medical test should I take?")
+    gr.Markdown("Our model will answer based on the content of the excel below, so please try to ask questions based on the table content.")
+    
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+    Initialization = gr.Button("Initialization")
+
+    def restart(history):
+        invitation = "ChatDoctor: "
+        human_invitation = "Patient: "
+        return [[" \n",invitation+" I am ChatDoctor, what medical questions do you have?"]]
+            
+    def user(user_message, history):
+        invitation = "ChatDoctor: "
+        human_invitation = "Patient: "
+        return "", history +[[human_invitation+user_message, None]]
+
+    def bot(history):
+        invitation = "ChatDoctor: "
+        human_invitation = "Patient: "
+        print(history)
+
+        
+        question = ""
+        for each_ques in history:
+            question = question+ each_ques[0].replace("Patient: ","")+" \n"
+
+        response = csv_prompter(question,csv_name)
+        
+        response = invitation+ response
+        history[-1][1] = response
+
+        return history
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False).then(restart, chatbot, chatbot)
+    Initialization.click(lambda: None, None, chatbot, queue=False).then(restart, chatbot, chatbot)
+    
+
+    
+if __name__ == "__main__":
+    demo.launch()
+