Create app.py

app.py

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+import streamlit as st
+import fitz  # PyMuPDF
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from openai import OpenAI
+from dotenv import load_dotenv
+import os
+
+# Load environment variables
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+
+# Initialize OpenAI client
+client = OpenAI(api_key=api_key)
+
+# Utility Functions
+def load_pdf(file):
+    """Extract text from a PDF file."""
+    try:
+        doc = fitz.open(stream=file.read(), filetype="pdf")
+        return "".join([page.get_text() for page in doc])
+    except Exception as e:
+        st.error(f"Error reading PDF: {e}")
+        return ""
+
+def split_text(text, chunk_size=1000, chunk_overlap=20):
+    """Split text into manageable chunks."""
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap, length_function=len, is_separator_regex=False
+    )
+    return text_splitter.create_documents([text])
+
+def create_and_load_db(chunks, persist_directory="pdf_embeddings"):
+    """Create and load ChromaDB."""
+    embeddings = HuggingFaceEmbeddings()
+    vectordb = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=persist_directory)
+    vectordb.persist()
+    return Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+
+def generate_response(context, question):
+    """Generate a response using OpenAI."""
+    try:
+        messages = [
+            {"role": "system", "content": "You are an assistant that answers questions based on PDF content."},
+            {"role": "user", "content": f"Context: {context}\n\nQuestion: {question}\n\nAnswer:"}
+        ]
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",  # Replace with preferred model
+            messages=messages,
+            max_tokens=150,
+        )
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        st.error(f"Error generating response: {e}")
+        return None
+
+# Main Application Logic
+def main():
+    st.title("PDF Chatbot with OpenAI")
+
+    # Sidebar: File upload
+    uploaded_file = st.sidebar.file_uploader("Upload a PDF", type=['pdf'])
+    prompt = st.text_input("Ask a Question", "")
+    submitted = st.button("Submit")
+
+    if submitted and uploaded_file:
+        pdf_text = load_pdf(uploaded_file)
+
+        if pdf_text:
+            st.write("PDF Content Loaded!")
+            chunks = split_text(pdf_text)
+            vectordb = create_and_load_db(chunks)
+
+            if prompt:
+                docs = vectordb.similarity_search(prompt)
+                if docs:
+                    context = docs[0].page_content
+                    response = generate_response(context, prompt)
+                    st.subheader("Generated Answer:")
+                    st.write(response)
+                else:
+                    st.warning("No relevant information found.")
+        else:
+            st.error("Unable to extract text from the PDF.")
+
+if __name__ == "__main__":
+    main()