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import gradio as gr | |
import google.generativeai as genai | |
import upload_pdf | |
import re | |
import os | |
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") | |
# 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.", | |
) | |
# Launch the Gradio app | |
iface.launch(debug=True,share=True) | |