Update genAI.py

genAI.py

@@ -1,273 +1,273 @@
-# import streamlit as st
-# import json
-# import torch
-# from transformers import AutoTokenizer, AutoModel
-# import faiss
-# import google.generativeai as genai
-# from flashrank.Ranker import Ranker, RerankRequest
-
-# # Configure Google Generative AI API Key
-# genai.configure(api_key="AIzaSyArG3gnpZHnzi10mMSnyOMhzYJBeAZEJUs")  # Replace with your API key
-
-# # Load and preprocess the uploaded file
-# def load_and_preprocess(uploaded_file):
-#     data = json.load(uploaded_file)
-#     passages = [f"Speaker: {item['speaker']}. Text: {item['text']}" 
-#                 for item in data if item["text"].strip()]
-#     return data, passages
-
-# # Load embedding model
-# def load_model(model_name="BAAI/bge-m3"):
-#     tokenizer = AutoTokenizer.from_pretrained(model_name)
-#     model = AutoModel.from_pretrained(model_name)
-#     return tokenizer, model
-
-# # Generate embeddings
-# def generate_embeddings(passages, tokenizer, model, batch_size=10, device="cuda" if torch.cuda.is_available() else "cpu"):
-#     model.to(device)
-#     embeddings = []
-#     for i in range(0, len(passages), batch_size):
-#         batch = passages[i:i + batch_size]
-#         inputs = tokenizer(batch, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
-#         with torch.no_grad():
-#             outputs = model(**inputs).last_hidden_state.mean(dim=1) 
-#         embeddings.append(outputs.cpu())
-#     embeddings = torch.cat(embeddings, dim=0) 
-#     return embeddings.numpy()
-
-# # Store embeddings in FAISS
-# def store_in_faiss(embeddings):
-#     dimension = embeddings.shape[1]
-#     index = faiss.IndexFlatL2(dimension)  
-#     index.add(embeddings) 
-#     return index
-
-# # Retrieve top-k passages
-# def retrieve_top_k(query, tokenizer, model, faiss_index, passages, k=5, device="cuda" if torch.cuda.is_available() else "cpu"):
-#     model.to(device)
-#     inputs = tokenizer([query], return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
-#     with torch.no_grad():
-#         query_embedding = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
-#     distances, indices = faiss_index.search(query_embedding, k)
-#     retrieved_passages = [passages[i] for i in indices[0]]
-#     return retrieved_passages
-
-# # Rerank passages using FlashRank Ranker
-# def rerank_passages(query, passages):
-#     formatted_passages = [{"text": passage} for passage in passages]
-#     ranker = Ranker(model_name="rank-T5-flan", cache_dir="/my_cache_dir")  # Adjust cache directory as needed
-#     rerank_request = RerankRequest(query=query, passages=formatted_passages)
-#     results = ranker.rerank(rerank_request)
-#     return results
-
-# # Generate a response using Gemini 1.5 Flash
-# def generate_response(reranked_passages, query):
-#     context = " ".join([passage["text"] for passage in reranked_passages])
-#     input_text = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
-#     model = genai.GenerativeModel("gemini-1.5-flash")
-#     response = model.generate_content(input_text)
-#     return response.text
-
-# # Streamlit app
-# def main():
-#     st.set_page_config(page_title="Chatbot with Document Upload", layout="wide")
-#     st.title("📄 Chatbot for Minutes of Meeting")
-    
-#     # Initialize session state
-#     if "chat_history" not in st.session_state:
-#         st.session_state.chat_history = []
-#     if "faiss_index" not in st.session_state:
-#         st.session_state.faiss_index = None
-#     if "passages" not in st.session_state:
-#         st.session_state.passages = None
-#     if "tokenizer" not in st.session_state or "model" not in st.session_state:
-#         st.session_state.tokenizer, st.session_state.model = load_model()
-
-#     # File uploader
-#     uploaded_file = st.file_uploader("Upload a JSON file for processing", type=["json"])
-#     if uploaded_file:
-#         st.write("Processing the file...")
-#         data, passages = load_and_preprocess(uploaded_file)
-#         st.session_state.passages = passages
-        
-#         # Generate embeddings and store in FAISS
-#         tokenizer, model = st.session_state.tokenizer, st.session_state.model
-#         embeddings = generate_embeddings(passages, tokenizer, model)
-#         st.session_state.faiss_index = store_in_faiss(embeddings)
-#         st.success("File processed and embeddings generated successfully!")
-
-#     # Chat interface
-#     if st.session_state.faiss_index:
-#         st.header("Ask a Question")
-#         user_query = st.text_input("Type your question here:")
-#         if user_query:
-#             # Retrieve and rerank passages
-#             top_k_passages = retrieve_top_k(user_query, st.session_state.tokenizer, st.session_state.model, st.session_state.faiss_index, st.session_state.passages)
-#             reranked_passages = rerank_passages(user_query, top_k_passages)
-            
-#             # Generate response
-#             response = generate_response(reranked_passages, user_query)
-            
-#             # Display response
-#             st.markdown(f"**Question:** {user_query}")
-#             st.markdown(f"**Answer:** {response}")
-            
-#             # Update chat history
-#             st.session_state.chat_history.append({"question": user_query, "answer": response})
-
-#     # Chat history
-#     if st.session_state.chat_history:
-#         st.header("Chat History")
-#         for chat in st.session_state.chat_history:
-#             st.markdown(f"**Q:** {chat['question']}")
-#             st.markdown(f"**A:** {chat['answer']}")
-
-# # Run the app
-# if __name__ == "__main__":
-#     main()
-
-import streamlit as st
-from streamlit_chat import message  
-import json
-import torch
-from transformers import AutoTokenizer, AutoModel
-import faiss
-import google.generativeai as genai
-from flashrank.Ranker import Ranker, RerankRequest
-from langchain.memory import ConversationBufferMemory
-from pydantic import BaseModel,ConfigDict
-
-
-genai.configure(api_key="AIzaSyArG3gnpZHnzi10mMSnyOMhzYJBeAZEJUs")  
-
-class CustomMemory(ConversationBufferMemory):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-def load_and_preprocess(uploaded_file):
-    data = json.load(uploaded_file)
-    passages = [f"Speaker: {item['speaker']}. Text: {item['text']}" 
-                for item in data if item["text"].strip()]
-    return data, passages
-
-
-def load_model(model_name="BAAI/bge-m3"):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModel.from_pretrained(model_name)
-    return tokenizer, model
-
-
-def generate_embeddings(passages, tokenizer, model, batch_size=10, device="cuda" if torch.cuda.is_available() else "cpu"):
-    model.to(device)
-    embeddings = []
-    for i in range(0, len(passages), batch_size):
-        batch = passages[i:i + batch_size]
-        inputs = tokenizer(batch, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
-        with torch.no_grad():
-            outputs = model(**inputs).last_hidden_state.mean(dim=1) 
-        embeddings.append(outputs.cpu())
-    embeddings = torch.cat(embeddings, dim=0) 
-    return embeddings.numpy()
-
-
-def store_in_faiss(embeddings):
-    dimension = embeddings.shape[1]
-    index = faiss.IndexFlatL2(dimension)  
-    index.add(embeddings) 
-    return index
-
-
-def retrieve_top_k(query, tokenizer, model, faiss_index, passages, k=5, device="cuda" if torch.cuda.is_available() else "cpu"):
-    model.to(device)
-    inputs = tokenizer([query], return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
-    with torch.no_grad():
-        query_embedding = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
-    distances, indices = faiss_index.search(query_embedding, k)
-    retrieved_passages = [passages[i] for i in indices[0]]
-    return retrieved_passages
-
-
-def rerank_passages(query, passages):
-    formatted_passages = [{"text": passage} for passage in passages]
-    ranker = Ranker(model_name="rank-T5-flan", cache_dir="/my_cache_dir")  # Adjust cache directory as needed
-    rerank_request = RerankRequest(query=query, passages=formatted_passages)
-    results = ranker.rerank(rerank_request)
-    return results
-
-
-def generate_response(context, query):
-    input_text = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
-    model = genai.GenerativeModel("gemini-1.5-flash")
-    response = model.generate_content(input_text)
-    return response.text
-
-
-def handle_userinput(user_question):
-    
-    top_k_passages = retrieve_top_k(user_question, st.session_state.tokenizer, st.session_state.model, st.session_state.faiss_index, st.session_state.passages)
-    reranked_passages = rerank_passages(user_question, top_k_passages)
-
-    
-    context = " ".join([passage["text"] for passage in reranked_passages])
-
-   
-    response = generate_response(context, user_question)
-
-    
-    st.session_state.memory.chat_memory.add_user_message(user_question)
-    st.session_state.memory.chat_memory.add_ai_message(response)
-
-    return response
-
-
-def main():
-    st.set_page_config(page_title="Chatbot with MoM Document Upload", layout="wide")
-    st.title("📄 Chatbot for Minutes of Meeting ")
-
-    
-    if "memory" not in st.session_state:
-        st.session_state.memory = CustomMemory(memory_key='chat_history', return_messages=True)
-    if "faiss_index" not in st.session_state:
-        st.session_state.faiss_index = None
-    if "passages" not in st.session_state:
-        st.session_state.passages = None
-    if "tokenizer" not in st.session_state or "model" not in st.session_state:
-        st.session_state.tokenizer, st.session_state.model = load_model()
-
-    
-    uploaded_file = st.file_uploader("Upload a JSON file for processing", type=["json"])
-    if uploaded_file:
-        st.write("Processing the file...")
-        data, passages = load_and_preprocess(uploaded_file)
-        st.session_state.passages = passages
-
-        
-        tokenizer, model = st.session_state.tokenizer, st.session_state.model
-        embeddings = generate_embeddings(passages, tokenizer, model)
-        st.session_state.faiss_index = store_in_faiss(embeddings)
-        st.success("File processed and embeddings generated successfully!")
-
-    
-    if st.session_state.faiss_index:
-        st.header("Ask a Question")
-        user_query = st.text_input("Type your question here:")
-        if user_query:
-            response = handle_userinput(user_query)
-
-            
-            if "chat_history_ui" not in st.session_state:
-                st.session_state.chat_history_ui = []  
-            
-            st.session_state.chat_history_ui.append({"role": "user", "content": user_query})
-            st.session_state.chat_history_ui.append({"role": "bot", "content": response})
-
-    
-    if "chat_history_ui" in st.session_state:
-        for i,chat in enumerate(st.session_state.chat_history_ui):
-            if chat["role"] == "user":
-                message(chat["content"], is_user=True,key=f"user_{i}")
-            else:
-                message(chat["content"], is_user=False,key=f"bot_{i}")
-
-
-if __name__ == "__main__":
-    main()
+# import streamlit as st
+# import json
+# import torch
+# from transformers import AutoTokenizer, AutoModel
+# import faiss
+# import google.generativeai as genai
+# from flashrank.Ranker import Ranker, RerankRequest
+
+# # Configure Google Generative AI API Key
+# genai.configure(api_key="AIzaSyArG3gnpZHnzi10mMSnyOMhzYJBeAZEJUs")  # Replace with your API key
+
+# # Load and preprocess the uploaded file
+# def load_and_preprocess(uploaded_file):
+#     data = json.load(uploaded_file)
+#     passages = [f"Speaker: {item['speaker']}. Text: {item['text']}" 
+#                 for item in data if item["text"].strip()]
+#     return data, passages
+
+# # Load embedding model
+# def load_model(model_name="BAAI/bge-m3"):
+#     tokenizer = AutoTokenizer.from_pretrained(model_name)
+#     model = AutoModel.from_pretrained(model_name)
+#     return tokenizer, model
+
+# # Generate embeddings
+# def generate_embeddings(passages, tokenizer, model, batch_size=10, device="cuda" if torch.cuda.is_available() else "cpu"):
+#     model.to(device)
+#     embeddings = []
+#     for i in range(0, len(passages), batch_size):
+#         batch = passages[i:i + batch_size]
+#         inputs = tokenizer(batch, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
+#         with torch.no_grad():
+#             outputs = model(**inputs).last_hidden_state.mean(dim=1) 
+#         embeddings.append(outputs.cpu())
+#     embeddings = torch.cat(embeddings, dim=0) 
+#     return embeddings.numpy()
+
+# # Store embeddings in FAISS
+# def store_in_faiss(embeddings):
+#     dimension = embeddings.shape[1]
+#     index = faiss.IndexFlatL2(dimension)  
+#     index.add(embeddings) 
+#     return index
+
+# # Retrieve top-k passages
+# def retrieve_top_k(query, tokenizer, model, faiss_index, passages, k=5, device="cuda" if torch.cuda.is_available() else "cpu"):
+#     model.to(device)
+#     inputs = tokenizer([query], return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
+#     with torch.no_grad():
+#         query_embedding = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
+#     distances, indices = faiss_index.search(query_embedding, k)
+#     retrieved_passages = [passages[i] for i in indices[0]]
+#     return retrieved_passages
+
+# # Rerank passages using FlashRank Ranker
+# def rerank_passages(query, passages):
+#     formatted_passages = [{"text": passage} for passage in passages]
+#     ranker = Ranker(model_name="rank-T5-flan", cache_dir="/my_cache_dir")  # Adjust cache directory as needed
+#     rerank_request = RerankRequest(query=query, passages=formatted_passages)
+#     results = ranker.rerank(rerank_request)
+#     return results
+
+# # Generate a response using Gemini 1.5 Flash
+# def generate_response(reranked_passages, query):
+#     context = " ".join([passage["text"] for passage in reranked_passages])
+#     input_text = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
+#     model = genai.GenerativeModel("gemini-1.5-flash")
+#     response = model.generate_content(input_text)
+#     return response.text
+
+# # Streamlit app
+# def main():
+#     st.set_page_config(page_title="Chatbot with Document Upload", layout="wide")
+#     st.title("📄 Chatbot for Minutes of Meeting")
+    
+#     # Initialize session state
+#     if "chat_history" not in st.session_state:
+#         st.session_state.chat_history = []
+#     if "faiss_index" not in st.session_state:
+#         st.session_state.faiss_index = None
+#     if "passages" not in st.session_state:
+#         st.session_state.passages = None
+#     if "tokenizer" not in st.session_state or "model" not in st.session_state:
+#         st.session_state.tokenizer, st.session_state.model = load_model()
+
+#     # File uploader
+#     uploaded_file = st.file_uploader("Upload a JSON file for processing", type=["json"])
+#     if uploaded_file:
+#         st.write("Processing the file...")
+#         data, passages = load_and_preprocess(uploaded_file)
+#         st.session_state.passages = passages
+        
+#         # Generate embeddings and store in FAISS
+#         tokenizer, model = st.session_state.tokenizer, st.session_state.model
+#         embeddings = generate_embeddings(passages, tokenizer, model)
+#         st.session_state.faiss_index = store_in_faiss(embeddings)
+#         st.success("File processed and embeddings generated successfully!")
+
+#     # Chat interface
+#     if st.session_state.faiss_index:
+#         st.header("Ask a Question")
+#         user_query = st.text_input("Type your question here:")
+#         if user_query:
+#             # Retrieve and rerank passages
+#             top_k_passages = retrieve_top_k(user_query, st.session_state.tokenizer, st.session_state.model, st.session_state.faiss_index, st.session_state.passages)
+#             reranked_passages = rerank_passages(user_query, top_k_passages)
+            
+#             # Generate response
+#             response = generate_response(reranked_passages, user_query)
+            
+#             # Display response
+#             st.markdown(f"**Question:** {user_query}")
+#             st.markdown(f"**Answer:** {response}")
+            
+#             # Update chat history
+#             st.session_state.chat_history.append({"question": user_query, "answer": response})
+
+#     # Chat history
+#     if st.session_state.chat_history:
+#         st.header("Chat History")
+#         for chat in st.session_state.chat_history:
+#             st.markdown(f"**Q:** {chat['question']}")
+#             st.markdown(f"**A:** {chat['answer']}")
+
+# # Run the app
+# if __name__ == "__main__":
+#     main()
+
+import streamlit as st
+from streamlit_chat import message  
+import json
+import torch
+from transformers import AutoTokenizer, AutoModel
+import faiss
+import google.generativeai as genai
+from flashrank.Ranker import Ranker, RerankRequest
+from langchain.memory import ConversationBufferMemory
+from pydantic import BaseModel,ConfigDict
+
+
+genai.configure(api_key="AIzaSyArG3gnpZHnzi10mMSnyOMhzYJBeAZEJUs")  
+
+class CustomMemory(ConversationBufferMemory):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+def load_and_preprocess(uploaded_file):
+    data = json.load(uploaded_file)
+    passages = [f"Speaker: {item['speaker']}. Text: {item['text']}" 
+                for item in data if item["text"].strip()]
+    return data, passages
+
+
+def load_model(model_name="BAAI/bge-m3"):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModel.from_pretrained(model_name)
+    return tokenizer, model
+
+
+def generate_embeddings(passages, tokenizer, model, batch_size=10, device="cuda" if torch.cuda.is_available() else "cpu"):
+    model.to(device)
+    embeddings = []
+    for i in range(0, len(passages), batch_size):
+        batch = passages[i:i + batch_size]
+        inputs = tokenizer(batch, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
+        with torch.no_grad():
+            outputs = model(**inputs).last_hidden_state.mean(dim=1) 
+        embeddings.append(outputs.cpu())
+    embeddings = torch.cat(embeddings, dim=0) 
+    return embeddings.numpy()
+
+
+def store_in_faiss(embeddings):
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)  
+    index.add(embeddings) 
+    return index
+
+
+def retrieve_top_k(query, tokenizer, model, faiss_index, passages, k=5, device="cuda" if torch.cuda.is_available() else "cpu"):
+    model.to(device)
+    inputs = tokenizer([query], return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
+    with torch.no_grad():
+        query_embedding = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
+    distances, indices = faiss_index.search(query_embedding, k)
+    retrieved_passages = [passages[i] for i in indices[0]]
+    return retrieved_passages
+
+
+def rerank_passages(query, passages):
+    formatted_passages = [{"text": passage} for passage in passages]
+    ranker = Ranker(model_name="rank-T5-flan", cache_dir="/app/.cache")  # Adjust cache directory as needed
+    rerank_request = RerankRequest(query=query, passages=formatted_passages)
+    results = ranker.rerank(rerank_request)
+    return results
+
+
+def generate_response(context, query):
+    input_text = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
+    model = genai.GenerativeModel("gemini-1.5-flash")
+    response = model.generate_content(input_text)
+    return response.text
+
+
+def handle_userinput(user_question):
+    
+    top_k_passages = retrieve_top_k(user_question, st.session_state.tokenizer, st.session_state.model, st.session_state.faiss_index, st.session_state.passages)
+    reranked_passages = rerank_passages(user_question, top_k_passages)
+
+    
+    context = " ".join([passage["text"] for passage in reranked_passages])
+
+   
+    response = generate_response(context, user_question)
+
+    
+    st.session_state.memory.chat_memory.add_user_message(user_question)
+    st.session_state.memory.chat_memory.add_ai_message(response)
+
+    return response
+
+
+def main():
+    st.set_page_config(page_title="Chatbot with MoM Document Upload", layout="wide")
+    st.title("📄 Chatbot for Minutes of Meeting ")
+
+    
+    if "memory" not in st.session_state:
+        st.session_state.memory = CustomMemory(memory_key='chat_history', return_messages=True)
+    if "faiss_index" not in st.session_state:
+        st.session_state.faiss_index = None
+    if "passages" not in st.session_state:
+        st.session_state.passages = None
+    if "tokenizer" not in st.session_state or "model" not in st.session_state:
+        st.session_state.tokenizer, st.session_state.model = load_model()
+
+    
+    uploaded_file = st.file_uploader("Upload a JSON file for processing", type=["json"])
+    if uploaded_file:
+        st.write("Processing the file...")
+        data, passages = load_and_preprocess(uploaded_file)
+        st.session_state.passages = passages
+
+        
+        tokenizer, model = st.session_state.tokenizer, st.session_state.model
+        embeddings = generate_embeddings(passages, tokenizer, model)
+        st.session_state.faiss_index = store_in_faiss(embeddings)
+        st.success("File processed and embeddings generated successfully!")
+
+    
+    if st.session_state.faiss_index:
+        st.header("Ask a Question")
+        user_query = st.text_input("Type your question here:")
+        if user_query:
+            response = handle_userinput(user_query)
+
+            
+            if "chat_history_ui" not in st.session_state:
+                st.session_state.chat_history_ui = []  
+            
+            st.session_state.chat_history_ui.append({"role": "user", "content": user_query})
+            st.session_state.chat_history_ui.append({"role": "bot", "content": response})
+
+    
+    if "chat_history_ui" in st.session_state:
+        for i,chat in enumerate(st.session_state.chat_history_ui):
+            if chat["role"] == "user":
+                message(chat["content"], is_user=True,key=f"user_{i}")
+            else:
+                message(chat["content"], is_user=False,key=f"bot_{i}")
+
+
+if __name__ == "__main__":
+    main()