Create chat_with_doc.py

chat_with_doc.py

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+import os
+import openai
+import streamlit as st
+import chunk  # Import the chunking function from chunk.py
+
+# Load environment variables
+from dotenv import load_dotenv
+load_dotenv()
+
+# Set the OpenAI API key
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Function to interact with OpenAI GPT model
+def get_openai_response(prompt, model="gpt-4", max_tokens=150):
+    response = openai.Completion.create(
+        engine=model,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        n=1,
+        stop=None,
+        temperature=0.7,
+    )
+    return response.choices[0].text.strip()
+
+# Streamlit UI
+st.title("Chat with Your Document")
+
+# Get the chunked data directly from chunk.py (assuming `chunk_text()` is already executed)
+# For example, you can load the chunked data from a file or directly invoke the chunking logic
+document_text = """Your long document text goes here..."""
+chunked_data = chunk.chunk_text(document_text)
+
+# Display chunked data
+st.write("Document has been chunked into the following parts:")
+for i, chunk_part in enumerate(chunked_data, 1):
+    st.write(f"**Chunk {i}:**\n{chunk_part}\n")
+
+# Input field for user to ask questions about the chunked document
+st.subheader("Ask a question about the document:")
+user_question = st.text_input("Your question")
+
+# Button to submit the question
+if st.button("Get Answer"):
+    if user_question:
+        # Create the prompt with the document chunks and user's question
+        prompt = f"Document Chunks: {chunked_data}\n\nQuestion: {user_question}\nAnswer:"
+        response = get_openai_response(prompt)
+        st.write(f"**Answer**: {response}")
+    else:
+        st.error("Please provide a question.")