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README.md

@@ -1,12 +1,2 @@
----
-title: CRMBot
-emoji: 🐢
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# CrmBot
+Crm Functionality Helper Bot Asistant

app.py

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+from flask import Flask, request, render_template
+from openai import OpenAI as OPENAI
+import os
+import gradio as gr
+import json
+
+client = OPENAI()
+client.api_key = os.environ.get("OPENAI_API_KEY")
+
+app = Flask(__name__)
+
+conversation_history=[]
+
+def handle_input(input_str : str):
+    global conversation_history
+
+    if len(conversation_history) >=20:
+        conversation_history = conversation_history[:1] + conversation_history[-10:]
+
+    conversation_history.append({"role": "user", "content": f"{input_str}"})
+    
+    content = " ".join([str(item) for item in conversation_history])
+    completion = client.chat.completions.create(
+        model="gpt-4", 
+        messages=conversation_history,
+        # temperature=temperature,
+        # max_tokens=max_tokens,
+        # presence_penalty=presence_penalty,
+        # frequency_penalty=frequency_penalty,
+        # top_p = top_p_input,
+        #stream = stream_input
+    )
+
+    
+    
+    message = completion.choices[0].message.content
+
+    conversation_history.append({"role": "assistant", "content": f"{message}"})
+    
+    return message
+
+def initialize():
+    f = open('templates/record_types.json')
+    crm_temlate_json = json.load(f)
+
+    global conversation_history
+    default_message = (f"""
+    Microsoft Dynamic CRM için kayıt olusturmada yardımcı bir asistan gibi davranmanı istiyorum.
+    Senin isin yalnizca CRM kayit olsuturma ve yonetme konusunda yardimci olmak, bu sebeple kullanici
+    CRM haricinde baska bir konuda konusmak isterse onca kibarca gorevini hatirlatarak istedigi islemi 
+    yapamayacagini belirtiyorsun. Kullanici seninle iletisime gectiginde kendini CRM Asistan Bot olarak 
+    tanitarak nasil yardimci olabilecegini soracaksin.
+    Kullanicinin yapmak istedigi isleme gore {crm_temlate_json} json verisi icinden 'Contact' yada 'Aktivite' 
+    templatelerine gore kullanicin girmesi gereken input verilerini isteyeceksin.
+    Kullanıcı seninle iletişime geçtiğinde amaçlarını ögrenip, amaclanan kayit icin ihtiyac olabilecek 
+    alan bilgilerini sirasiyla, adim adim kullanicidan isteyecek ve islemler bittiginda bu bilgileri 
+    json olarak doneceksin. Kullanici kayit olusturman icin onaylamadigi surece
+    ek bilgi ekleyip eklemek istemedigini soracaksin. Ne olursa olsun kullanicinin belirtmesi gereken bilgileri kendin girmeyeceksin.
+    Kullanici ek bilgi eklemek isterse sirasiyla o bilgileri isteyerek olusturacagin kayit bilgisine ekleyeceksin.
+    Ornek: [Kullanici:"Merhaba", Asistan:"Merhaba! Size nasil yardimci olabilirim?", 
+    Kullanici: "Kontakt kaydi olusturmak istiyorum", ...]. Ilk kayit ile baslayalim
+    """)
+
+    conversation_history.append({"role": "system", "content": f"{default_message}"})
+
+    completion = client.chat.completions.create(
+        model="gpt-4", 
+        messages=[{"role": "system", "content": " ".join([str(item) for item in conversation_history])}],
+        temperature=0.3,
+        max_tokens=7500,
+    )
+
+    message = completion.choices[0].message.content
+    print(message)
+
+@app.route('/')
+def my_form():
+    return render_template('my-form.html')
+
+@app.route('/send_message', methods=['POST'])
+def send_message():
+    text = request.form['text']
+    answer = handle_input(text)
+    return answer
+
+
+if __name__ == '__main__':
+    initialize()
+    app.run()

client

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+import requests
+
+def send_message_to_server(text):
+    url = 'http://localhost:5000/send_message'  # Flask servisinizin URL'si
+    data = {'text': text}
+    response = requests.post(url, data=data)
+    return response.text
+
+if __name__ == '__main__':
+    while True:
+        user_input = input("Mesajınızı girin (çıkmak için 'exit' yazın): ")
+        if user_input.lower() == 'exit':
+            break
+        server_response = send_message_to_server(user_input)
+        print("Sunucudan gelen yanıt:", server_response)

main.py

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+from openai import OpenAI as OPENAI
+import os
+import gradio as gr
+import json
+
+client = OPENAI()
+client.api_key = os.environ.get("OPENAI_API_KEY")
+
+
+conversation_history=[]
+
+def predict(input):
+
+    completion = client.completion.create(
+    model="gpt-4", 
+    messages=[{"role": "user", "content": input}]
+    )
+    response = completion.choices[0].messag.content
+
+    return response
+
+
+
+def initial_prompt():
+
+    # client.api_key = os.environ.get("OPENAI_API_KEY")
+
+    f = open('templates/record_types.json')
+    crm_temlate_json = json.load(f)
+
+    global conversation_history
+    default_message = (f"""
+    Microsoft Dynamic CRM için kayıt olusturmada yardımcı bir asistan gibi davranmanı istiyorum.
+    Senin isin yalnizca CRM kayit olsuturma ve yonetme konusunda yardimci olmak, bu sebeple kullanici
+    CRM haricinde baska bir konuda konusmak isterse onca kibarca gorevini hatirlatarak istedigi islemi 
+    yapamayacagini belirtiyorsun. Kullanici seninle iletisime gectiginde kendini CRM Asistan Bot olarak 
+    tanitarak nasil yardimci olabilecegini soracaksin.
+    Kullanicinin yapmak istedigi isleme gore {crm_temlate_json} json verisi icinden 'Contact' yada 'Aktivite' 
+    templatelerine gore kullanicin girmesi gereken input verilerini isteyeceksin.
+    Kullanıcı seninle iletişime geçtiğinde amaçlarını ögrenip, amaclanan kayit icin ihtiyac olabilecek 
+    alan bilgilerini sirasiyla, adim adim kullanicidan isteyecek ve islemler bittiginda bu bilgileri 
+    json olarak doneceksin. Kullanici kayit olusturman icin onaylamadigi surece
+    ek bilgi ekleyip eklemek istemedigini soracaksin. Ne olursa olsun kullanicinin belirtmesi gereken bilgileri kendin girmeyeceksin.
+    Kullanici ek bilgi eklemek isterse sirasiyla o bilgileri isteyerek olusturacagin kayit bilgisine ekleyeceksin.
+    Ornek: [Kullanici:"Merhaba", Asistan:"Merhaba! Size nasil yardimci olabilirim?", 
+    Kullanici: "Kontakt kaydi olusturmak istiyorum", ...]. Ilk kayit ile baslayalim
+    """)
+
+    conversation_history.append({"role": "system", "content": f"{default_message}"})
+
+    completion = client.chat.completions.create(
+        model="gpt-4", 
+        messages=[{"role": "system", "content": " ".join([str(item) for item in conversation_history])}],
+        temperature=0.3,
+        max_tokens=7500,
+    )
+
+    message = completion.choices[0].message.content
+    print(message)
+
+initial_prompt()
+
+def handle_input(input_str : str):
+    global conversation_history
+
+    if len(conversation_history) >=20:
+        conversation_history = conversation_history[:1] + conversation_history[-10:]
+
+    conversation_history.append({"role": "user", "content": f"{input_str}"})
+    
+    content = " ".join([str(item) for item in conversation_history])
+    completion = client.chat.completions.create(
+        model="gpt-4", 
+        messages=conversation_history,
+        # temperature=temperature,
+        # max_tokens=max_tokens,
+        # presence_penalty=presence_penalty,
+        # frequency_penalty=frequency_penalty,
+        # top_p = top_p_input,
+        #stream = stream_input
+    )
+
+    
+    
+    message = completion.choices[0].message.content
+
+    conversation_history.append({"role": "assistant", "content": f"{message}"})
+
+    def get_response_again(content): 
+
+        while True:
+            completion = client.chat.completion.create(
+                model="gpt-4", 
+                messages=[{"role": "assistant", "content": content}],
+                # temperature=temperature,
+                # max_tokens=max_tokens,
+                # presence_penalty=presence_penalty,
+                # frequency_penalty=frequency_penalty,
+                # top_p = top_p_input,
+                #stream = stream_input
+            )
+
+            message = completion.choices[0].message.content
+
+            if "Müşteri" not in message:
+                break
+
+        return message
+
+    # if "Müşteri" in message:
+    #     get_response_again(content)
+
+    # conversation_history.append(f"{message}\n")
+    
+    return message
+
+def get_model_reply(query,context=[]):
+ 
+    context += [query]
+    
+    # client.api_key = api_key
+
+    response = handle_input(query)
+
+    
+    context += [response]
+    
+    responses = [(u,b) for u,b in zip(context[::2], context[1::2])]
+    
+    return responses, context
+
+
+#TODO Thiws feature will be added later
+def speech_2_text(audio,api_key, context=[]):
+    client.api_key = api_key
+    audio_file= open(audio, "rb")
+    transcript = client.Audio.transcribe("whisper-1", audio_file)
+    prompt = transcript.text
+    context += [prompt]
+
+    completion = client.chat.completion.create(
+        model="gpt-4", 
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=2500,
+    )
+
+    response = completion.choices[0].message.content
+    context += [response]
+    
+    responses = [(u,b) for u,b in zip(context[::2], context[1::2])]
+
+    return responses, context
+
+# def get_audio(url,api_key,context=[]):
+    client.api_key = api_key
+    destination = f"./audio/test"
+    try:
+        ydl_opts = {
+            'format': 'bestaudio/best',
+            'outtmpl':destination,
+            'postprocessors': [{
+                'key': 'FFmpegExtractAudio',
+                'preferredcodec': 'mp3',
+                'preferredquality': '192',
+            }],
+        }
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            ydl.download([url])
+        
+        audio_file= open(f'{destination}.mp3', "rb")
+        transcript = client.Audio.transcribe("whisper-1", audio_file)
+
+        context += [transcript.text]
+        
+        responses = [(u,b) for u,b in zip(context[::2], context[1::2])]
+
+        return responses, context
+    
+    except Exception as e:
+        print("Connection Error")
+
+
+with gr.Blocks(css="#chatbot {overflow-y:auto; height:400px;}") as dialog_app:
+
+    with gr.Tab("ChatBot"):
+        with gr.Row():
+            # with gr.Column(scale=1, min_width=600):
+                # api_key = gr.Textbox(label="Your API Key", type="password")
+                # temperature_input = gr.Slider(minimum=0, maximum=1.0, default=0.5, step=0.01, label="Temperature")
+                # max_tokens_input = gr.inputs.Slider(minimum=1, maximum=2048, default=50, step=10, label="Max Tokens")
+                # top_p_input = gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.5, step=0.01, label="Top P")
+                # presence_penalty_input = gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.0, step=0.1, label="Presence Penalty")
+                # frequency_penalty_input = gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.0, step=0.1, label="Frequency Penalty")
+                #stream_input = gr.inputs.Checkbox(label="Stream")
+
+            with gr.Column(scale=2, min_width=600):
+                chatbot = gr.Chatbot(elem_id="chatbot")
+                state = gr.State([])
+
+                txt = gr.Textbox(
+                    show_label=False, 
+                    placeholder="Enter text and press enter",
+                    container=False
+                )
+
+        # txt.submit(get_model_reply, [txt, api_key, temperature_input, max_tokens_input,top_p_input, presence_penalty_input,frequency_penalty_input, state], [chatbot, state])
+        txt.submit(get_model_reply, [txt, state], [chatbot, state])
+        txt.submit(lambda :"", None, txt)
+
+    
+    # with gr.Tab("Voice Chat"):
+    #     with gr.Row():
+    #         with gr.Column(scale=1, min_width=600):
+    #             voice_api_key = gr.Textbox(label="Your API Key", type="password")
+    #             voice_state = gr.State([])
+    #             youtube_url = gr.Textbox(
+    #                 show_label=False, 
+    #                 type="text", 
+    #                 placeholder="Enter an Youtube URL")
+
+    #             mic_audio = gr.Audio(source="microphone", type="filepath")
+
+    #             with gr.Row():
+    #                 with gr.Column(scale=1, min_width=250):
+    #                     audio_clean_btn = gr.Button(value='Clean')
+    #                 with gr.Column(scale=2, min_width=250):
+    #                     audio_sbmt_btn = gr.Button(value='Submit', variant='primary')
+
+    #         with gr.Column(scale=2, min_width=600):
+    #             voice_bot = gr.Chatbot(elem_id="chatbot")
+    #             #txt_box = gr.Textbox(type="text")
+    #             voice_state = gr.State([])
+
+    #             txt = gr.Textbox(
+    #                 show_label=False, 
+    #                 placeholder="Enter text and press enter"
+    #             ).style(container=False)
+            
+    #         #youtube_url.submit(get_audio, [youtube_url,voice_api_key, state], [voice_bot, state])
+    #         #audio_clean_btn.click(clean_audio, )
+    #         audio_sbmt_btn.click(speech_2_text, inputs=[mic_audio,voice_api_key,voice_state], outputs=[voice_bot, voice_state])
+
+    #         txt.submit(get_model_reply, [txt, voice_api_key, temperature_input, max_tokens_input,top_p_input, presence_penalty_input,frequency_penalty_input, voice_state], [voice_bot, voice_state])
+    #         txt.submit(lambda :"", None, txt)
+    
+
+dialog_app.launch()