Update app.py

app.py

@@ -6,29 +6,34 @@ from langchain_huggingface import HuggingFaceEmbeddings
 from pinecone import Pinecone, ServerlessSpec
 from langchain_community.document_loaders import WhatsAppChatLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
+from dotenv import load_dotenv
 
-# Initialize Hugging Face embeddings
-embeddings = HuggingFaceEmbeddings()
+#load env var
+load_dotenv()
 
-def load_chat_content(pinecone_key, file) -> str:
-    """Load chat content from the uploaded zip file and store it in Pinecone."""
-    # Initialize Pinecone
-    pc = Pinecone(api_key=pinecone_key)
-    index_name = "whatsapp-group-chat-index"
-    
-    if index_name not in pc.list_indexes().names():
-        pc.create_index(
-            name=index_name,
-            dimension=768,  # change as per embedding model
-            metric="cosine",
-            spec=ServerlessSpec(
-                cloud='aws',
-                region='us-east-1'
-            )
+# Initialize Pinecone and the index outside the function
+pinecone_key = os.getenv("PINECONE_API_KEY")
+pc = Pinecone(api_key=pinecone_key)
+index_name = "whatsapp-chat-index"
+
+if index_name not in pc.list_indexes().names():
+    pc.create_index(
+        name=index_name,
+        dimension=768,  # change as per embedding model
+        metric="cosine",
+        spec=ServerlessSpec(
+            cloud='aws',
+            region='us-east-1'
         )
+    )
 
-    index = pc.Index(index_name)
+index = pc.Index(index_name)
 
+# Initialize Hugging Face embeddings
+embeddings = HuggingFaceEmbeddings()
+
+def load_chat_content(file) -> str:
+    """Load chat content from the uploaded zip file and store it in Pinecone."""
     # Load and process the ZIP file
     temp_dir = 'temp_extracted_files'
     os.makedirs(temp_dir, exist_ok=True)
@@ -57,20 +62,32 @@ def load_chat_content(pinecone_key, file) -> str:
     
     # Store chunks in Pinecone with unique IDs
     vectors_to_upsert = []
+    unique_ids = []
     for i, chunk in enumerate(chunks):
         vector = embeddings.embed_documents([chunk.page_content])[0]
         unique_id = str(uuid.uuid4())  # Generate a unique ID
         vectors_to_upsert.append((unique_id, vector, {"text": chunk.page_content}))
+        unique_ids.append(unique_id)
     
+    # Upsert vectors to Pinecone
     index.upsert(vectors_to_upsert)
 
-    return "Chat content has been successfully upserted to Pinecone."
+    # Verify insertion by querying Pinecone with the unique IDs
+    inserted_ids = []
+    for uid in unique_ids:
+        result = index.fetch(ids=[uid])
+        if uid in result["matches"]:
+            inserted_ids.append(uid)
+    
+    if len(inserted_ids) == len(unique_ids):
+        return "All chat content has been successfully upserted to Pinecone."
+    else:
+        return f"Insertion incomplete. Only {len(inserted_ids)} out of {len(unique_ids)} chunks were inserted."
 
 # Define the Gradio interface
 interface = gr.Interface(
     fn=load_chat_content,
     inputs=[
-        gr.Textbox(label="Enter Pinecone API Key", type="password"),
         gr.File(label="Upload WhatsApp Chat Zip File")
     ],
     outputs="text",
@@ -79,4 +96,4 @@ interface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()