Create app.py

app.py

@@ -0,0 +1,78 @@
+!git clone https://github.com/sudipmondal1310/Internship.git
+!pip install llama-index==0.5.6
+!pip install langchain==0.0.148
+from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
+from langchain import OpenAI
+import sys
+import os
+from IPython.display import Markdown, display
+
+def construct_index(directory_path):
+    # set maximum input size
+    max_input_size = 4096
+    # set number of output tokens
+    num_outputs = 2000
+    # set maximum chunk overlap
+    max_chunk_overlap = 20
+    # set chunk size limit
+    chunk_size_limit = 600 
+
+    # define prompt helper
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+
+    # define LLM
+    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
+ 
+    documents = SimpleDirectoryReader(directory_path).load_data()
+    
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
+
+    index.save_to_disk('index.json')
+
+    return index
+
+os.environ["OPENAI_API_KEY"]="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"
+
+construct_index("/content/Internship/Data")
+
+pip install gradio
+
+
+def ask_ai_new(query):
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+    while True: 
+        #query = input("What do you want to ask? ")
+        response = index.query(query)
+        response = Markdown(f"<b>{response.response}</b>")
+        print(response)
+        return response.data
+
+import time
+import random
+import gradio as gr
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    query = gr.inputs.Textbox(label="Enter your message here")
+    clear = gr.Button("Clear")
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        bot_message = ask_ai_new(history[-1][0])
+        print(bot_message)
+        history[-1][1] = ""
+        for character in bot_message:
+            history[-1][1] += character
+            time.sleep(0.05)
+            yield history
+
+    query.submit(user, [query, chatbot], [query, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue()
+demo.launch()