Create app.py

app.py

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+import streamlit as st
+from tempfile import NamedTemporaryFile
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain import PromptTemplate, LLMChain
+from langchain.llms import HuggingFaceHub
+
+# Function to save the uploaded PDF to a temporary file
+def save_uploaded_file(uploaded_file):
+    with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+        temp_file.write(uploaded_file.read())
+        return temp_file.name
+
+# Streamlit UI
+st.title("PDF Question Answering App")
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+if uploaded_file is not None:
+    # Save the uploaded file to a temporary location
+    temp_file_path = save_uploaded_file(uploaded_file)
+
+    # Load the PDF document using PyPDFLoader
+    loader = PyPDFLoader(temp_file_path)
+    pages = loader.load_and_split()
+
+    # Initialize embeddings and Chroma
+    embed = HuggingFaceEmbeddings()
+    db = Chroma.from_documents(pages, embed)
+    
+    import os
+
+    # Set the Hugging Face API token as an environment variable
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
+
+
+
+    # Initialize the HuggingFaceHub with the appropriate task
+    llm = HuggingFaceHub(
+        repo_id="Manoj21k/GPT4ALL",
+        model_kwargs={"temperature": 1e-10}
+    )
+
+
+    # Define a function to get answers
+    def get_answer(question):
+        doc = db.similarity_search(question, k=4)
+        context = doc[0].page_content + doc[1].page_content + doc[2].page_content + doc[3].page_content
+
+        max_seq_length = 512  # You may define this based on your model
+
+        context = context[:max_seq_length]
+
+        # Prompt template
+        template = """Use the following pieces of context to answer the question at the end. 
+        If you don't know the answer, just say that you don't know, don't try to make up an answer.
+        Context: {context} and
+        Question: {question}
+        Answer: """
+
+        prompt = PromptTemplate(template=template, input_variables=["context", "question"]).partial(context=context)
+
+        llm_chain = LLMChain(prompt=prompt, llm=llm)
+        output = llm_chain.run(context=context, question=question, max_length=512)
+
+        answer_index = output.find("Answer:")
+        next_line_index = output.find("\n", answer_index)
+        answer_content = output[answer_index + len("Answer:")-1:next_line_index]
+
+        return answer_content
+
+    question = st.text_input("Enter your question:")
+    if st.button("Get Answer"):
+        answer = get_answer(question)
+        st.write("Answer:")
+        st.write(answer)
+
+    # Cleanup: Delete the temporary file
+    os.remove(temp_file_path)