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Update app.py
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app.py
CHANGED
@@ -19,16 +19,43 @@ def initialize(pdf_file, question):
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# Process the PDF
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pdf_loader = PyPDFLoader(file_path)
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pages = pdf_loader.load_and_split()
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# Configure Google Generative AI (replace with your API key)
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model = genai.GenerativeModel('gemini-pro')
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model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
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stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
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stuff_answer = stuff_chain(
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return
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else:
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return "Error: The uploaded file could not be found."
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else:
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@@ -56,3 +83,5 @@ interface.launch()
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# Process the PDF
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pdf_loader = PyPDFLoader(file_path)
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pages = pdf_loader.load_and_split()
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processed_context = "\n".join(str(page.page_content) for page in pages[:30]) # Limit to first 30 pages
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# Configure Google Generative AI (replace with your API key)
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genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
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# Prompt template for formatting context and question
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prompt_template = """Answer the question as precise as possible using the provided context. If the answer is not contained in the context, say "answer not available in context"
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Context:
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{context}
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Question:
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{question}
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Answer:
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"""
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prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
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# Generate answer using GeminiPro model
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# Load the GeminiPro model
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model = genai.GenerativeModel('gemini-pro')
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# Prepare the input data
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input_data = {
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"context": processed_context,
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"question": question
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}
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# Generate the answer using load_qa_chain
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stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
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stuff_answer = stuff_chain(input_data, return_only_outputs=True)
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# Extract the answer
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generated_answer = stuff_answer['output_text']
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return generated_answer
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else:
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return "Error: The uploaded file could not be found."
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else:
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