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Parent(s):
bf852d8
Update app.py
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app.py
CHANGED
@@ -4,42 +4,10 @@ import gradio as gr
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import os
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import google.generativeai as genai
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GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
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import chromadb
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from langchain.document_loaders import PyPDFLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from uuid import uuid4
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import gradio as gr
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# Now you can use hugging_face_api_key in your code
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genai.configure(api_key=GOOGLE_API_KEY)
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model = genai.GenerativeModel('gemini-pro') # Load the model
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def get_Answer(query):
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res = collection.query( # Assuming `collection` is defined elsewhere
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query_texts=query,
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n_results=2
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)
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system = f"""You are a teacher. You will be provided some context,
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your task is to analyze the relevant context and answer the below question:
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- {query}
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"""
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context = " ".join([re.sub(r'[^\x00-\x7F]+', ' ', r) for r in res['documents'][0]])
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prompt = f"### System: {system} \n\n ###: User: {context} \n\n ### Assistant:\n"
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answer = model.generate_content(prompt).text
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return answer
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# # Define the Gradio interface
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# iface = gr.Interface(
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# fn=get_Answer,
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# inputs=gr.Textbox(lines=5, placeholder="Ask a question"), # Textbox for query
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# outputs="textbox", # Display the generated answer in a textbox
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# title="Answer Questions with Gemini-Pro",
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# description="Ask a question and get an answer based on context from a ChromaDB collection.",
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# )
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@@ -67,51 +35,70 @@ def upload_pdf(file_path):
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return f"PDF Uploaded Successfully. {collection.count()} chunks stored in ChromaDB"
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# outputs="textbox", # Display the output text in a textbox
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# title="Upload PDF to ChromaDB",
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# description="Upload a PDF file and store its text chunks in ChromaDB.",
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# )
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iface1 = gr.Interface(
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fn=get_Answer,
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inputs=gr.Textbox(lines=5, placeholder="Ask a question"),
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outputs="textbox",
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title="Answer Questions with Gemini-Pro",
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description="Ask a question and get an answer based on context from a ChromaDB collection.",
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)
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iface2 = gr.Interface(
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fn=upload_pdf,
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inputs=["file"],
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outputs="textbox",
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title="Upload PDF to ChromaDB",
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description="Upload a PDF file and store its text chunks in ChromaDB.",
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)
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)
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thread1.start()
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thread2.start()
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import os
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import google.generativeai as genai
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GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
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import chromadb
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from langchain.document_loaders import PyPDFLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from uuid import uuid4
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)
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return f"PDF Uploaded Successfully. {collection.count()} chunks stored in ChromaDB"
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# Now you can use hugging_face_api_key in your code
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genai.configure(api_key=GOOGLE_API_KEY)
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model = genai.GenerativeModel('gemini-pro') # Load the model
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def get_Answer(query):
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res = collection.query( # Assuming `collection` is defined elsewhere
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query_texts=query,
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n_results=2
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)
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system = f"""You are a teacher. You will be provided some context,
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your task is to analyze the relevant context and answer the below question:
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- {query}
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"""
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context = " ".join([re.sub(r'[^\x00-\x7F]+', ' ', r) for r in res['documents'][0]])
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prompt = f"### System: {system} \n\n ###: User: {context} \n\n ### Assistant:\n"
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answer = model.generate_content(prompt).text
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return answer
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# Define the Gradio interface
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iface1 = gr.Interface(
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fn=get_Answer,
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inputs=gr.Textbox(lines=5, placeholder="Ask a question"), # Textbox for query
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outputs="textbox", # Display the generated answer in a textbox
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title="Answer Questions with Gemini-Pro",
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description="Ask a question and get an answer based on context from a ChromaDB collection.",
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)
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# Define the Gradio interface
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iface2 = gr.Interface(
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fn=upload_pdf,
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inputs=["file"], # Specify a file input component
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outputs="textbox", # Display the output text in a textbox
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title="Upload PDF to ChromaDB",
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description="Upload a PDF file and store its text chunks in ChromaDB.",
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)
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def check_boxes(checkbox_values):
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if checkbox_values == "iface1":
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return iface1.launch(debug=True, share=True)
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else:
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return iface1.launch(debug=True, share=True)
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checkboxes = [
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gr.Checkbox(label="iface1"),
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gr.Checkbox(label="iface2"),
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]
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iface = gr.Interface(
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check_boxes,
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inputs=checkboxes,
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outputs="text",
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title="Checkbox Demo",
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description="Select one or both checkboxes.",
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iface.launch(debug=True, share=True)
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