Delete app.py

app.py

@@ -1,99 +0,0 @@
-import os
-import time
-
-import gradio as gr
-import pandas as pd
-from model import Model
-from tqdm import tqdm
-
-tqdm.pandas()
-
-OUTPUT_FILE = "./results_qa.csv"
-
-def new_vote(data: gr.LikeData, question, model_name, **kwargs):
-    feedback = "Good" if data.liked else "Bad"
-    df = pd.read_csv(OUTPUT_FILE)
-    df['Feedback'] = df.apply(lambda x: feedback if (x.Model == model_name and x.Question == question) else None, axis = 1)
-    df.to_csv(OUTPUT_FILE, index=False)
-    
-# def answer_question(question: str, model_name: str, system_prompt: str):
-#     start_time = time.time()
-#     qa_model = Model(model_name=model_name)
-#     response, sources = qa_model.run(system_prompt=system_prompt, query=question)
-#     time_taken = time.time() - start_time
-#     words = len(question) + len(response)
-#     efficiency = words / time_taken
-#     final_response = f"{response} \n\nTime Taken: {time_taken}"
-#     new_row = {'Model': model_name, 'Question': question, 'Answer': response, "Sources": sources, "Time": time_taken, "Words": words, "Efficiency": efficiency, "Feedback": None, "final_response": final_response}
-#     if os.path.isfile(OUTPUT_FILE):
-#         df = pd.read_csv(OUTPUT_FILE)
-#         rows = df.values.tolist()
-#         # print("df.values.tolist(): ", df.values.tolist())
-#         # df = df.append(new_row, ignore_index=True)
-#         rows.append(new_row)
-#     else:
-#         rows = [new_row]
-#     df = pd.DataFrame(rows)
-#     df.to_csv(OUTPUT_FILE, index=False)
-#     yield [(question, final_response)]
-
-def answer_question(question: str, model_name: str, system_prompt: str):
-    start_time = time.time()
-    qa_model = Model(model_name=model_name)
-    gen_response = qa_model.run(system_prompt=system_prompt, query=question)
-    response = ""
-    for resp in gen_response:
-        if isinstance(resp, list):
-            sources = resp
-            break
-        resp = resp.replace("$", "₹") 
-        response += resp
-        yield [(question, response)], OUTPUT_FILE
-        
-    time_taken = time.time() - start_time
-    words = len(question) + len(response)
-    efficiency = words / time_taken
-    temp_sources = "\n".join([f"{i + 1}. {d}" for i, d in enumerate(sources)])
-    final_response = f"{response} \n\nSources: \n{temp_sources} \n\nTime Taken: {time_taken}"
-    new_row = {'Model': model_name, 'Question': question, 'Answer': response, "Sources": sources, "Time": time_taken, "Words": words, "Efficiency": efficiency, "Feedback": None, "final_response": final_response}
-    if os.path.isfile(OUTPUT_FILE):
-        try:
-            df = pd.read_csv(OUTPUT_FILE)
-            rows = df.to_dict(orient="records")
-            rows.append(new_row)
-        except Exception:
-            rows = [new_row]
-    else:
-        rows = [new_row]
-    
-    df = pd.DataFrame(rows)
-    df.to_csv(OUTPUT_FILE, index=False)
-    final_response = final_response.strip("Question").strip("\n")
-    final_response = final_response.strip("\n").strip(" ").strip("Answer:").strip("Question").strip("\n").replace("Answer:", "")
-    yield [(question, final_response)], OUTPUT_FILE
-    
-    
-
-if __name__ == "__main__":
-    with gr.Blocks() as demo:
-        chatbot = gr.Chatbot()
-        
-        # with gr.Row():
-        
-        textbox = gr.Textbox(label="Query")
-        # system_prompt = """Answer the question using the context. Provide examples only from the context and use only Rupees (₹) in examples. If you don't know the answer, just say 'Please rephrase the question I am unable to answer'"""
-        system_prompt = """"Answer the question using the context. Provide examples only from the context and use only Rupees (₹) in examples. If you don't know the answer, just say 'Please rephrase the question I am unable to answer'"""
-        system_prompt = "Use the following pieces of book to answer the question at the end. \nIf you don't know the answer, please think rationally and answer from the book"
-        system_prompt = """Answer the question using the context. Provide examples only from the context and use only Rupees (₹) in examples. If you don't know the answer, just say 'Please rephrase the question I am unable to answer'"""
-        system_prompt = """Answer the question from the book. Provide examples only from the book. If you don't know the answer, just say 'Please rephrase the question'"""
-        
-        choices=["gpt4", "gpt-3.5-turbo"]
-        
-        system_prompt = gr.Textbox(value=system_prompt, label="System Prompt")
-        model_name = gr.Dropdown(choices=choices, value="gpt-3.5-turbo", label="Model")
-        file = gr.File(value = OUTPUT_FILE, file_types=["csv"], label="Output")
-        textbox.submit(answer_question, [textbox, model_name, system_prompt], [chatbot, file])
-        chatbot.like(new_vote, [textbox, model_name], None)    
-    
-    demo.queue()
-    demo.launch(share=True)