Update app.py

app.py

@@ -1,63 +1,83 @@
+import os
+import glob
+from pathlib import Path
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import nest_asyncio
+
+# Ensure async compatibility in Jupyter
+nest_asyncio.apply()
+
+# Import OpenAI key with helper function
+from helper import get_openai_api_key
+OPENAI_API_KEY = get_openai_api_key()
+
+# Define the path to the directory containing the PDF files
+folder_path = 'Ehlers-Danlos-1'
+
+# Get the list of all PDF files in the directory
+pdf_files = glob.glob(os.path.join(folder_path, '*.pdf'))
+print(pdf_files)
+
+# Extract just the filenames (optional)
+pdf_filenames = [os.path.basename(pdf) for pdf in pdf_files]
+print(pdf_filenames)
+
+# Import utilities
+from utils import get_doc_tools
+
+# Truncate function names if necessary
+def truncate_function_name(name, max_length=64):
+    return name if len(name) <= max_length else name[:max_length]
+
+# Create tools for each PDF
+paper_to_tools_dict = {}
+for pdf in pdf_files:
+    print(f"Getting tools for paper: {pdf}")
+    vector_tool, summary_tool = get_doc_tools(pdf, Path(pdf).stem)
+    paper_to_tools_dict[pdf] = [vector_tool, summary_tool]
+
+# Combine all tools into a single list
+all_tools = [t for pdf in pdf_files for t in paper_to_tools_dict[pdf]]
+
+# Define an object index and retriever over these tools
+from llama_index.core import VectorStoreIndex
+from llama_index.core.objects import ObjectIndex
+
+obj_index = ObjectIndex.from_objects(
+    all_tools,
+    index_cls=VectorStoreIndex,
 )
 
+obj_retriever = obj_index.as_retriever(similarity_top_k=3)
+
+# Initialize the OpenAI LLM
+from llama_index.llms.openai import OpenAI
+llm = OpenAI(model="gpt-3.5-turbo")
+
+# Set up the agent
+from llama_index.core.agent import FunctionCallingAgentWorker
+from llama_index.core.agent import AgentRunner
+
+agent_worker = FunctionCallingAgentWorker.from_tools(
+    tool_retriever=obj_retriever,
+    llm=llm, 
+    verbose=True
+)
+agent = AgentRunner(agent_worker)
+
+# Define the function to query the agent
+def ask_agent(question):
+    response = agent.query(question)
+    return str(response)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=ask_agent,
+    inputs="text",
+    outputs="text",
+    title="EDS Diagnosis Helper",
+    description="Ask questions related to Ehlers-Danlos Syndrome diagnosis.",
+)
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio app
+iface.launch()