Update app.py

app.py

@@ -1,17 +1,18 @@
 import gradio as gr
-from langchain_community.llms import GooglePalm
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_community.embeddings import GooglePalmEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from secret1 import GOOGLE_API as google_api
 import PyPDF2
-def chatbot_response(user_input):
-    # This is a placeholder function. Replace with your actual chatbot logic.
-    # bot_response = "You said: " + user_input
-    # history.append((user_input, bot_response))
-    return "hi"
+from langchain.embeddings import GooglePalmEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.llms import GooglePalm
+
+# Define chatbot response function
+def chatbot_response(user_input, history):
+    # Example: returning a placeholder response, update with actual chatbot logic
+    bot_response = "You said: " + user_input
+    history.append((user_input, bot_response))
+    return bot_response, history
 
+# Define text splitter function
 def text_splitter_function(text):
     text_splitter = CharacterTextSplitter(
         separator = '\n',
@@ -20,51 +21,48 @@ def text_splitter_function(text):
         length_function = len,
     )
     texts = text_splitter.split_text(text)
-    return texts;
-
+    return texts
 
+# Helper function for text processing
 def helper(text_splitter):
-    db = FAISS.from_texts(text_splitter, embeddings);
-    return 'hi';
-    
+    db = FAISS.from_texts(text_splitter, embeddings)  # Use 'embeddings' for FAISS
+    return 'hi'
+
+# PDF text extraction function
 def text_extract(file):
     pdf_reader = PyPDF2.PdfReader(file.name)
-    # Get the number of pages
     num_pages = len(pdf_reader.pages)
-    # Extract text from each page
     text = ""
     for page_num in range(num_pages):
         page = pdf_reader.pages[page_num]
-        text += page.extract_text()
-    text_splitter=text_splitter_function(text);
-    
-    result=helper(text_splitter);
+        text += page.extract_text() or ""
+    text_splitter = text_splitter_function(text)  # Split extracted text into chunks
+    result = helper(text_splitter)  # Call helper to process text chunks
     return result
-# db = FAISS.from_texts(text_splitter, embeddings);
-#     retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})
-#     llm=GooglePalm(google_api_key=google_api)
-#     qa = RetrievalQA.from_chain_type(
-#             llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True
-#         )
-#     result=qa.invoke("where is tajmahal")
 
-    
-    
+# Define Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Chat with ChatGPT-like Interface")
 
     chatbot = gr.Chatbot()
     state = gr.State([])
+
     with gr.Row():
         with gr.Column():
             user_input = gr.Textbox(show_label=False, placeholder="Type your message here...")
             send_btn = gr.Button("Send")
         with gr.Column():
-            input_file=gr.File(label="Upload PDF", file_count="single")
-            submit_btn=gr.Button("Submit")
-    # submit_btn.click(text_extract, [input_file], [user_input])
-    send_btn.click(chatbot_response,inputs=[user_input,state],outputs=[chatbot, state])
+            input_file = gr.File(label="Upload PDF", file_count="single")
+            submit_btn = gr.Button("Submit")
+    
+    # Connect submit button to text_extract function
+    submit_btn.click(text_extract, inputs=[input_file], outputs=[user_input])
+
+    # Connect send button to chatbot_response function
+    send_btn.click(chatbot_response, inputs=[user_input, state], outputs=[chatbot, state])
 
+# Initialize embeddings and launch the app
 if __name__ == "__main__":
-    embeddings=GooglePalmEmbeddings(google_api_key=google_api)
+    google_api_key = "YOUR_GOOGLE_API_KEY"  # Replace with your actual Google API key
+    embeddings = GooglePalmEmbeddings(google_api_key=google_api_key)
     demo.launch()