Update app.py

app.py

@@ -1,44 +1,41 @@
+from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+from langchain.chat_models import ChatOpenAI
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-model = AutoModelForCausalLM.from_pretrained(
-    "tiiuae/falcon-7b-instruct",
-    torch_dtype=torch.bfloat16,
-    trust_remote_code=True,
-    device_map="auto",
-    low_cpu_mem_usage=True,
-)
-tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
-
-
-def generate_text(input_text):
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
-    attention_mask = torch.ones(input_ids.shape)
-
-    output = model.generate(
-        input_ids,
-        attention_mask=attention_mask,
-        max_length=200,
-        do_sample=True,
-        top_k=10,
-        num_return_sequences=1,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-
-    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    print(output_text)
-
-    # Remove Prompt Echo from Generated Text
-    cleaned_output_text = output_text.replace(input_text, "")
-    return cleaned_output_text
-
-
-text_generation_interface = gr.Interface(
-    fn=generate_text,
-    inputs=[
-        gr.inputs.Textbox(label="Input Text"),
-    ],
-    outputs=gr.inputs.Textbox(label="Generated Text"),
-    title="Falcon-7B Instruct",
-).launch()
+import sys
+import os
+import openai
+
+os.environ["OPENAI_API_KEY"] = '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'
+openai.api_key = "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"
+
+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=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    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.components.Textbox(lines=7, label="Enter your text"),
+                     outputs="text",
+                     title="Custom-trained AI Chatbot")
+
+index = construct_index("docs")
+iface.launch(share=True)