Create app.py

app.py

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+import gradio as gr
+import os
+import time
+from dotenv import load_dotenv
+from pinecone import Pinecone, ServerlessSpec
+from langchain_pinecone import PineconeVectorStore
+from langchain_openai import OpenAIEmbeddings
+from langchain_core.documents import Document
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+import io
+import pandas as pd
+
+# Load environment variables from a .env file
+load_dotenv()
+
+# --- Backend Functions ---
+
+def get_stored_files():
+    """Retrieve a list of files currently stored in the Pinecone index"""
+    try:
+        pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
+        index_name = os.environ.get("PINECONE_INDEX_NAME")
+
+        existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]
+        if index_name not in existing_indexes:
+            return []
+
+        index = pc.Index(index_name)
+        # Query with a dummy vector to fetch metadata. Increase top_k if you have more files.
+        results = index.query(vector=[0.0] * 3072, top_k=10000, include_metadata=True)
+
+        unique_files = set()
+        if results.matches:
+            for match in results.matches:
+                if hasattr(match, 'metadata') and match.metadata and 'source' in match.metadata:
+                    unique_files.add(match.metadata['source'])
+
+        return sorted(list(unique_files))
+    except Exception as e:
+        print(f"Error retrieving stored files: {str(e)}")
+        return []
+
+def delete_file_from_vectorstore(filename):
+    """Deletes all vectors associated with a specific filename from Pinecone."""
+    if not filename:
+        return "No file selected for deletion.", get_files_df()
+    try:
+        pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
+        index_name = os.environ.get("PINECONE_INDEX_NAME")
+        index = pc.Index(index_name)
+        index.delete(filter={"source": {"$eq": filename}})
+
+        return f"Successfully deleted {filename}.", get_files_df()
+    except Exception as e:
+        return f"Error while deleting the file: {str(e)}", get_files_df()
+
+
+def embedder(uploaded_file_path):
+    """Handles embedding of the uploaded PDF file."""
+    if uploaded_file_path is None:
+        return "No file uploaded. Please upload a PDF.", get_files_df()
+
+    try:
+        original_filename = os.path.basename(uploaded_file_path)
+
+        pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
+        index_name = os.environ.get("PINECONE_INDEX_NAME")
+
+        existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]
+        if index_name not in existing_indexes:
+            pc.create_index(
+                name=index_name,
+                dimension=3072,
+                metric="cosine",
+                spec=ServerlessSpec(cloud="aws", region="us-east-1"),
+            )
+            while not pc.describe_index(index_name).status["ready"]:
+                time.sleep(1)
+
+        index = pc.Index(index_name)
+        embeddings = OpenAIEmbeddings(model="text-embedding-3-large", api_key=os.environ.get("OPENAI_API_KEY"))
+        vector_store = PineconeVectorStore(index=index, embedding=embeddings)
+
+        loader = PyPDFLoader(uploaded_file_path)
+        raw_documents = loader.load()
+
+        for doc in raw_documents:
+            doc.metadata['source'] = original_filename
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=800,
+            chunk_overlap=400,
+            length_function=len,
+            is_separator_regex=False,
+        )
+        documents = text_splitter.split_documents(raw_documents)
+
+        uuids = [f"{original_filename.replace('.pdf', '')}_{i+1}" for i in range(len(documents))]
+
+        batch_size = 100
+        for i in range(0, len(documents), batch_size):
+            batch_docs = documents[i:i+batch_size]
+            batch_ids = uuids[i:i+batch_size]
+            vector_store.add_documents(documents=batch_docs, ids=batch_ids)
+
+        return f"File '{original_filename}' successfully embedded!", get_files_df()
+
+    except Exception as e:
+        return f"Unable to create embeddings: {str(e)}", get_files_df()
+
+
+# --- Gradio Interface Functions ---
+def get_files_df():
+    files = get_stored_files()
+    if files:
+        return pd.DataFrame({"Stored Files": files})
+    else:
+        return pd.DataFrame({"Stored Files": []})
+
+# CORRECTION: Updated function to handle the select event correctly
+def handle_file_selection(evt: gr.SelectData):
+    """
+    Handles the file selection event from the DataFrame.
+    evt.value contains the value of the selected cell.
+    """
+    if evt.value:
+        return evt.value
+    return ""
+
+# --- Gradio UI ---
+with gr.Blocks(theme=gr.themes.Soft(), title="PDF Uploader") as demo:
+    gr.Markdown("# PDF File Uploader for Chatbot")
+    gr.Markdown("Upload PDF files to add their content to the chatbot's knowledge base.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## 📤 Upload New File")
+            file_uploader = gr.File(
+                label="Upload your PDF file",
+                file_types=[".pdf"],
+                type="filepath"
+            )
+            upload_button = gr.Button("Upload to Chatbot Memory", variant="primary")
+            upload_status = gr.Markdown("")
+
+        with gr.Column(scale=1):
+            gr.Markdown("## 🗂️ Stored Files")
+
+            refresh_button = gr.Button("Refresh File List")
+
+            file_df = gr.DataFrame(
+                value=get_files_df,
+                headers=["Stored Files"],
+                interactive=True
+            )
+
+            selected_file_text = gr.Textbox(
+                label="Selected File",
+                interactive=False,
+                placeholder="Click on a file above to select it"
+            )
+
+            delete_button = gr.Button("🗑️ Delete Selected File", variant="stop")
+            delete_status = gr.Markdown("")
+
+    # --- Event Handlers ---
+    upload_button.click(
+        fn=embedder,
+        inputs=[file_uploader],
+        outputs=[upload_status, file_df]
+    )
+
+    refresh_button.click(
+        fn=get_files_df,
+        inputs=[],
+        outputs=[file_df]
+    )
+
+    # CORRECTION: Removed the 'inputs' argument.
+    # The event data 'evt' is now passed automatically to 'handle_file_selection'.
+    file_df.select(
+        fn=handle_file_selection,
+        inputs=None,  # Explicitly setting to None or removing this line works
+        outputs=[selected_file_text]
+    )
+
+    delete_button.click(
+        fn=delete_file_from_vectorstore,
+        inputs=[selected_file_text],
+        outputs=[delete_status, file_df]
+    )
+
+if __name__ == "__main__":
+    demo.launch()