new update

app.py

@@ -1,7 +1,42 @@
+from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+from langchain import OpenAI
+# from gpt_index import LLMPredictor, ServiceContext
+from gpt_index import LLMPredictor, ServiceContext
 import gradio as gr
+import sys
+import os
 
-def greet(name):
-    return "Hello From Hormuud" + name + "!!"
+ 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def construct_index(directory_path):
+    max_input_size = 4096
+    num_outputs = 512
+    max_chunk_overlap = 20
+    chunk_size_limit = 600
+
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+
+    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.9, 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 = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+
+    index.save_to_disk('index.json')
+
+    return index
+
+def chatbot(input_text):
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+    response = index.query(input_text, response_mode="compact")
+    return response.response
+
+iface = gr.Interface(fn=chatbot,
+                     inputs=gr.inputs.Textbox(lines=7, label="Enter your text"),
+                     outputs="text",
+                     title="Hormuud Services")
+
+# index = construct_index("docs")
+iface.launch(share=True)

requirement.txt

@@ -0,0 +1,2 @@
+gpt_index
+langchain