Update app.py

app.py

@@ -4,42 +4,10 @@ import gradio as gr
 import os
 import google.generativeai as genai
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-
-
 import chromadb
 from langchain.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from uuid import uuid4
-import gradio as gr
-
-# Now you can use hugging_face_api_key in your code
-
-genai.configure(api_key=GOOGLE_API_KEY)
-model = genai.GenerativeModel('gemini-pro')  # Load the model
-
-def get_Answer(query):
-    res = collection.query(  # Assuming `collection` is defined elsewhere
-        query_texts=query,
-        n_results=2
-    )
-    system = f"""You are a teacher. You will be provided some context, 
-    your task is to analyze the relevant context and answer the below question:
-    - {query}
-    """
-    context = " ".join([re.sub(r'[^\x00-\x7F]+', ' ', r) for r in res['documents'][0]])
-    prompt = f"### System: {system} \n\n ###: User: {context} \n\n ### Assistant:\n"
-    answer = model.generate_content(prompt).text
-    return answer
-
-# # Define the Gradio interface
-# iface = gr.Interface(
-#     fn=get_Answer,
-#     inputs=gr.Textbox(lines=5, placeholder="Ask a question"),  # Textbox for query
-#     outputs="textbox",  # Display the generated answer in a textbox
-#     title="Answer Questions with Gemini-Pro",
-#     description="Ask a question and get an answer based on context from a ChromaDB collection.",
-# )
-
 
 
 
@@ -67,51 +35,70 @@ def upload_pdf(file_path):
     )
     return f"PDF Uploaded Successfully. {collection.count()} chunks stored in ChromaDB"
 
-# # Define the Gradio interface
-# iface = gr.Interface(
-#     fn=upload_pdf,
-#     inputs=["file"],  # Specify a file input component
-#     outputs="textbox",  # Display the output text in a textbox
-#     title="Upload PDF to ChromaDB",
-#     description="Upload a PDF file and store its text chunks in ChromaDB.",
-# )
+# Now you can use hugging_face_api_key in your code
+
+genai.configure(api_key=GOOGLE_API_KEY)
+model = genai.GenerativeModel('gemini-pro')  # Load the model
 
-# Gradio interfaces
+def get_Answer(query):
+    res = collection.query(  # Assuming `collection` is defined elsewhere
+        query_texts=query,
+        n_results=2
+    )
+    system = f"""You are a teacher. You will be provided some context, 
+    your task is to analyze the relevant context and answer the below question:
+    - {query}
+    """
+    context = " ".join([re.sub(r'[^\x00-\x7F]+', ' ', r) for r in res['documents'][0]])
+    prompt = f"### System: {system} \n\n ###: User: {context} \n\n ### Assistant:\n"
+    answer = model.generate_content(prompt).text
+    return answer
+
+# Define the Gradio interface
 iface1 = gr.Interface(
     fn=get_Answer,
-    inputs=gr.Textbox(lines=5, placeholder="Ask a question"),
-    outputs="textbox",
+    inputs=gr.Textbox(lines=5, placeholder="Ask a question"),  # Textbox for query
+    outputs="textbox",  # Display the generated answer in a textbox
     title="Answer Questions with Gemini-Pro",
     description="Ask a question and get an answer based on context from a ChromaDB collection.",
 )
+
+
+
+# Define the Gradio interface
 iface2 = gr.Interface(
     fn=upload_pdf,
-    inputs=["file"],
-    outputs="textbox",
+    inputs=["file"],  # Specify a file input component
+    outputs="textbox",  # Display the output text in a textbox
     title="Upload PDF to ChromaDB",
     description="Upload a PDF file and store its text chunks in ChromaDB.",
 )
 
 
 
-# thread1 = threading.Thread(target=iface1.launch, args=(debug=True, share=True, server_port=7861))
-# thread2 = threading.Thread(target=iface2.launch, args=(debug=True, share=True, server_port=7862))
+def check_boxes(checkbox_values):
+    if checkbox_values == "iface1":
+        return iface1.launch(debug=True, share=True)
+    else:
+        return iface1.launch(debug=True, share=True)
 
-debug = True  # Define the variables
-share = True
+checkboxes = [
+    gr.Checkbox(label="iface1"),
+    gr.Checkbox(label="iface2"),
+]
 
-thread1 = threading.Thread(
-    target=iface1.launch, args=(debug, share, server_port:=8080)  # Pass the variables
-)
-thread2 = threading.Thread(
-    target=iface2.launch, args=(debug, share, server_port:=8081)  # Pass the variables
+iface = gr.Interface(
+    check_boxes,
+    inputs=checkboxes,
+    outputs="text",
+    title="Checkbox Demo",
+    description="Select one or both checkboxes.",
 )
 
+iface.launch(debug=True, share=True)
 
 
 
-thread1.start()
-thread2.start()