Upload app.py

app.py

@@ -1,21 +1,76 @@
+from dotenv import load_dotenv
+import os
 import streamlit as st
+import openai
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document
+from sentence_transformers import CrossEncoder
+import fitz  # PyMuPDF library for PDF processing
+import tempfile
 
-def extract_text(text):
-    # Extract the first 100 characters
-    extracted_text = text[:100]
-    return extracted_text
-
-# Streamlit UI
-st.title('Text Extraction App')
-
-# Text input
-user_input = st.text_area('Enter your text here:', '')
-
-# Button to trigger extraction
-if st.button('Extract Text'):
-    if user_input:
-        extracted_text = extract_text(user_input) + st.secrets['api_key']
-        st.write('Extracted Text:')
-        st.write(extracted_text)
-    else:
-        st.warning('Please enter some text before extracting.')
+load_dotenv()
+
+openai.api_key = os.getenv(st.secrets['api_key'])
+
+# Create a sidebar
+st.sidebar.title("Model Configuration")
+
+# File uploader moved to the sidebar
+uploaded_file = st.sidebar.file_uploader("Upload a PDF", type=["pdf"])
+
+# Option menu for model selection
+model_selection = st.sidebar.selectbox("Model Selection", ["GPT 3.5", "LLama 2"])
+
+# Slider for selecting model temperature
+model_temperature = st.sidebar.slider("Select model temperature", 0.0, 0.5, 1.0)
+
+# Initialize LLM response storage
+llm_responses = []
+
+# Initialize HHEM model
+hhem_model = CrossEncoder('vectara/hallucination_evaluation_model')
+
+if uploaded_file is not None:
+    # Save the uploaded PDF file to a temporary location
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
+        temp_pdf.write(uploaded_file.read())
+        temp_pdf_path = temp_pdf.name
+
+    # Open the PDF file using PyMuPDF
+    pdf_document = fitz.open(temp_pdf_path)
+
+    text = ""
+    for page_number in range(pdf_document.page_count):
+        page = pdf_document[page_number]
+        text += page.get_text()
+
+    documents = [Document(text=text)]
+
+    index = VectorStoreIndex.from_documents(documents)
+    query_engine = index.as_query_engine()
+
+    query = st.text_input("Ask your question")
+
+    button = st.button("Ask")
+
+    if button:
+        print(query)
+        response = query_engine.query(query)
+        st.write(response.response)
+
+        # Record LLM response
+        llm_responses.append(response.response)
+
+        # Calculate and display HHEM score for each LLM response
+        for i, llm_response in enumerate(llm_responses):
+            score = hhem_model.predict([text, llm_response])
+            st.sidebar.write(f"Response {i + 1} - HHEM Score: {score}")
+
+    # Close and remove the temporary PDF file
+    pdf_document.close()
+    os.remove(temp_pdf_path)
+
+# Display LLM responses
+if llm_responses:
+    st.sidebar.markdown("## LLM Responses")
+    for i, llm_response in enumerate(llm_responses):
+        st.sidebar.write(f"Response {i + 1}: {llm_response}")