Upload 3 files

app.py

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+import os
+from gpt_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+from langchain import OpenAI
+import gradio as gr
+import openai
+
+API_URL = "https://api.openai.com/v1/chat/completions"
+
+openai.api_key = os.environ["OPENAI_API_KEY"]
+
+top_p_chatgpt = 1.0
+temperature_chatgpt = 1.0
+
+def predict_chatgpt(inputs,chat_counter_chatgpt, chatbot_chatgpt=[], history=[]):
+    if chat_counter_chatgpt != 0:
+        messages = []
+        for data in chatbot_chatgpt:
+            temp1 = {}
+            temp1["role"] = "user"
+            temp1["content"] = data[0]
+            temp2 = {}
+            temp2["role"] = "assistant"
+            temp2["content"] = data[1]
+            messages.append(temp1)
+            messages.append(temp2)
+        temp3 = {}
+        temp3["role"] = "user"
+        temp3["content"] = inputs
+        messages.append(temp3)
+
+    #os.environ['OPENAI_API_KEY'] =  openai.api_key
+    chat_counter_chatgpt += 1
+    history.append("You asked: " + inputs)
+   
+    # load index from disk
+    index = GPTSimpleVectorIndex.load_from_disk('PLIndex.json')
+
+    # query the index
+    result = index.query(inputs)
+    response = result.response.split()
+    token_counter = 0
+    partial_words = ""
+    counter = 0
+    for chunk in response:
+                partial_words=partial_words+" "+chunk
+                if token_counter == 0:
+                    history.append(" " + partial_words)
+                else:
+                    history[-1] = partial_words
+                chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)]  # convert to tuples of list
+                token_counter += 1
+                yield chat, history, chat_counter_chatgpt  # This resembles {chatbot: chat, state: history}
+
+
+def reset_textbox():
+    return gr.update(value="")
+
+def reset_chat(chatbot, state):
+    return None, []
+with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
+                #chatgpt {height: 400px; overflow: auto;}} """, theme=gr.themes.Default(primary_hue="slate") ) as PLCoversationalAI:
+    with gr.Row():
+      with gr.Column(scale=14):
+          with gr.Box():
+              with gr.Row():
+                  with gr.Column(scale=13):
+                   inputs = gr.Textbox(label="Ask anything about Productization Labs ⤵️    Try : Who is Gopala" )
+                  with gr.Column(scale=1):
+                      b1 = gr.Button('Submit', elem_id = 'submit').style(full_width=True)
+                      b2 = gr.Button('Clear', elem_id = 'clear').style(full_width=True)
+                  state_chatgpt = gr.State([])
+
+          with gr.Box():
+              with gr.Row():
+                  chatbot_chatgpt = gr.Chatbot(elem_id="chatgpt", label="Productization Labs Conversational AI")
+    chat_counter_chatgpt = gr.Number(value=0, visible=False, precision=0)
+             
+
+    inputs.submit(reset_textbox, [], [inputs])
+    
+    b1.click( predict_chatgpt, 
+                [ inputs, chat_counter_chatgpt, chatbot_chatgpt, state_chatgpt], 
+                [chatbot_chatgpt, state_chatgpt],)
+
+    b2.click(reset_chat, [chatbot_chatgpt, state_chatgpt], [chatbot_chatgpt, state_chatgpt])
+
+    PLCoversationalAI.queue(concurrency_count=16).launch(height= 2500, debug=True)
+
+
+
+
+

requirements.txt

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+openai
+gpt_index
+langchain
+transformers