uploaded app

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+import os
+
+os.environ["OPENAI_API_KEY"] = "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"
+os.environ["SERPAPI_API_KEY"] = "8ccb7553d2c890be7bbbdc41e5ced77ab3732dfea760e42da3711778c98c074c"
+
+
+# Define a function to load PDF and perform processing
+def process_pdf(pdf_path):
+    pdfreader = PdfReader(pdf_path)
+
+    raw_text = ''
+    for page in pdfreader.pages:
+        content = page.extract_text()
+        if content:
+            raw_text += content
+
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=800,
+        chunk_overlap=100,
+        length_function=len,
+    )
+    texts = text_splitter.split_text(raw_text)
+
+    embeddings = OpenAIEmbeddings()
+    document_search = FAISS.from_texts(texts, embeddings)
+
+    chain = load_qa_chain(OpenAI(), chain_type="stuff")
+
+    return document_search, chain
+
+# Function to get yes/no emoji based on answer content
+def get_answer_emoji(answer):
+    answer = answer.lower()
+    if "yes" in answer:
+        return "✅"
+    elif "no" in answer:
+        return "❌"
+    else:
+        return "🟡"
+
+# Streamlit UI
+st.title("OrangePro AI - PDF and Text Analysis")
+
+# Upload a PDF file
+uploaded_pdf_file = st.file_uploader("Upload a PDF file for analysis", type=["pdf"])
+uploaded_text_file = st.file_uploader("Upload a text file with questions (if available)", type=["txt"])
+
+if uploaded_pdf_file:
+    st.subheader("Selected PDF Content")
+
+    # Display the content of the PDF
+    pdf_reader, qa_chain = process_pdf(uploaded_pdf_file)
+    
+    st.write("PDF Content:")
+    st.text(pdf_reader)
+
+    if uploaded_text_file:
+        st.warning("Questions will be extracted from the uploaded text file. Disabling question input below.")
+        text_content = uploaded_text_file.read().decode('utf-8')  # Decode bytes to string
+        questions = text_content.splitlines()
+    else:
+        # Allow the user to enter a list of questions
+        questions = st.text_area("Enter a list of questions (one per line):").split('\n')
+    
+    if st.button("Analyze Questions"):
+        # Perform question answering for each question
+        st.subheader("Answers:")
+        answer_summary = []
+        yes_count = 0
+        total_questions = len(questions)
+
+        for question in questions:
+            if question.strip() == "":
+                continue
+            docs = pdf_reader.similarity_search(question)
+            answer = qa_chain.run(input_documents=docs, question=question)
+            
+            emoji = get_answer_emoji(answer)
+            answer_summary.append([question, answer, emoji])
+
+            if emoji == "✅":
+                yes_count += 1
+
+        # Calculate and display the percentage of "yes" answers
+        if total_questions > 0:
+            yes_percentage = (yes_count / total_questions) * 100
+        else:
+            yes_percentage = 0
+
+        answer_summary.append(["Percentage of 'Yes' Answers", f"{yes_percentage:.2f}%", ""])
+        
+        # Display the summary in a table
+        st.table(answer_summary)
+
+# About section
+st.sidebar.title("About OrangePro AI")
+st.sidebar.info(
+    "OrangePro AI is an artificial intelligence testing and benchmarking platform for large language models (LLMs). It scores model performance based on real-world scenarios, allowing corporate clients such as Fortune 500 companies to choose the best model for their specific use cases."
+    "\n\n"
+    "The platform automates scoring, ranking model performance in real-world scenarios and key criteria like hallucinations and safety. OrangePro AI also automatically generates adversarial test suites at a large scale and benchmarks models to help customers identify the best model for specific use cases."
+)
+
+# Footer
+st.sidebar.text("Powered by Streamlit and Langchain")