Update app.py

app.py

@@ -18,10 +18,46 @@ from Preprocessing2 import handle_categorical_values, missing_values, handle_dup
 from RAG import create_doucment, ask_me, load_models_embedding, load_models_llm, create_database
 
 
+
+# Helper Functions
+def create_documents(df):
+    """Converts a DataFrame into a list of Document objects."""
+    documents = [
+        Document(
+            metadata={"id": str(i)},
+            page_content=json.dumps(row.to_dict())
+        )
+        for i, row in df.iterrows()
+    ]
+    return documents
+
+def load_embedding_model():
+    """Loads the embedding model for vectorization."""
+    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+def load_llm(api_key):
+    """Loads the LLM model for answering queries."""
+    return HuggingFaceHub(
+        repo_id="Qwen/Qwen2.5-72B-Instruct",
+        huggingfacehub_api_token=api_key,
+        model_kwargs={"temperature": 0.5, "max_length": 100}
+    )
+
+def ask_question(question, retriever, llm):
+    """Uses a QA chain to retrieve and answer a question."""
+    qa_chain = RetrievalQA.from_chain_type(
+        retriever=retriever,
+        chain_type="stuff",
+        llm=llm,
+        return_source_documents=False
+    )
+    response = qa_chain.invoke({"query": question})
+    return response["result"]
+
+# Streamlit App
 def upload_data():
     st.title("Upload Dataset")
-    file = st.file_uploader("Upload your dataset", type=[
-                            "csv", "xlsx"], key="file_uploader_1")
+    file = st.file_uploader("Upload your dataset", type=["csv", "xlsx"])
 
     if file:
         try:
@@ -34,26 +70,14 @@ def upload_data():
             st.success("Dataset uploaded successfully!")
         except Exception as e:
             st.error(f"Error loading file: {e}")
-    return file
-
-
-def download_data():
-    """Downloads the DataFrame as a CSV file."""
-    if "data" in st.session_state and not st.session_state["data"].empty:
-        csv = st.session_state["data"].to_csv(index=False).encode('utf-8')
-
-        st.download_button(
-            label="Download Cleaned Dataset",
-            data=csv,
-            file_name="cleaned_data.csv",
-            mime="text/csv"
-        )
 
+def preview_data():
+    if "data" in st.session_state:
+        st.title("Preview Dataset")
+        st.dataframe(st.session_state["data"])
     else:
-        st.warning(
-            "No data available to download. Please modify or upload a dataset first.")
-
-
+        st.warning("Please upload a dataset first.")
+api="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21"
 def rag_chatbot():
     st.title("RAG Chatbot")
 
@@ -63,78 +87,159 @@ def rag_chatbot():
 
         # Convert data to documents
         st.write("Processing the dataset...")
-        documents = create_doucment(df)
-        st.write(f"Created {len(documents)} documents.")
+        documents = create_documents(df)
 
         # Load models
         st.write("Loading models...")
-        embedding = load_models_embedding()
-        llm = load_models_llm()
+        embedding_model = load_embedding_model()
+        llm_model = load_llm(api_key=api[:-2])
 
         # Create retriever
-        retriever = create_database(embedding, documents).as_retriever()
+        retriever = FAISS.from_documents(documents, embedding=embedding_model).as_retriever()
 
         # Ask a question
         question = st.text_input("Ask a question about your dataset:")
         if question:
-            response = ask_me(question, retriever, llm)
+            response = ask_question(question, retriever, llm_model)
             st.write(f"Answer: {response}")
     else:
         st.warning("Please upload a dataset to proceed.")
 
-
 def main():
     st.sidebar.title("Navigation")
     options = st.sidebar.radio(
         "Go to",
-        [
-            "Upload",
-            "Preview",
-            "Data Cleaning",
-            "Modify Column Names",
-            "General Data Statistics",
-            "Describe",
-            "Info",
-            "Handle Categorical",
-            "Missing Values",
-            "Handle Duplicates",
-            "Handle Outliers",
-            "Download",
-            "RAG Chatbot"
-        ],
-        key="unique_navigation_key",
+        ["Upload", "Preview", "RAG Chatbot"],
+        key="navigation_key"
     )
 
     if options == "Upload":
         upload_data()
     elif options == "Preview":
         preview_data()
-    elif options == "Data Cleaning":
-        data_cleaning()
-    elif options == "Modify Column Names":
-        modify_column_names()
-    elif options == "General Data Statistics":
-        show_general_data_statistics()
-    elif options == "Describe":
-        describe_data()
-    elif options == "Info":
-        info_data()
-    elif options == "Handle Categorical":
-        handle_categorical_values()
-    elif options == "Missing Values":
-        missing_values()
-    elif options == "Handle Duplicates":
-        handle_duplicates()
-    elif options == "Handle Outliers":
-        handle_outliers()
-    elif options == "Download":
-        download_data()
     elif options == "RAG Chatbot":
         rag_chatbot()
 
-    else:
-        st.warning("Please upload a dataset first.")
-
-
 if __name__ == "__main__":
     main()
+
+# def upload_data():
+#     st.title("Upload Dataset")
+#     file = st.file_uploader("Upload your dataset", type=[
+#                             "csv", "xlsx"], key="file_uploader_1")
+
+#     if file:
+#         try:
+#             if file.name.endswith(".csv"):
+#                 data = pd.read_csv(file)
+#             elif file.name.endswith(".xlsx"):
+#                 data = pd.read_excel(file)
+
+#             st.session_state["data"] = data
+#             st.success("Dataset uploaded successfully!")
+#         except Exception as e:
+#             st.error(f"Error loading file: {e}")
+#     return file
+
+
+# def download_data():
+#     """Downloads the DataFrame as a CSV file."""
+#     if "data" in st.session_state and not st.session_state["data"].empty:
+#         csv = st.session_state["data"].to_csv(index=False).encode('utf-8')
+
+#         st.download_button(
+#             label="Download Cleaned Dataset",
+#             data=csv,
+#             file_name="cleaned_data.csv",
+#             mime="text/csv"
+#         )
+
+#     else:
+#         st.warning(
+#             "No data available to download. Please modify or upload a dataset first.")
+
+
+# def rag_chatbot():
+#     st.title("RAG Chatbot")
+
+#     # Check if data is uploaded
+#     if "data" in st.session_state and isinstance(st.session_state["data"], pd.DataFrame):
+#         df = st.session_state["data"]
+
+#         # Convert data to documents
+#         st.write("Processing the dataset...")
+#         documents = create_doucment(df)
+#         st.write(f"Created {len(documents)} documents.")
+
+#         # Load models
+#         st.write("Loading models...")
+#         embedding = load_models_embedding()
+#         llm = load_models_llm()
+
+#         # Create retriever
+#         retriever = create_database(embedding, documents).as_retriever()
+
+#         # Ask a question
+#         question = st.text_input("Ask a question about your dataset:")
+#         if question:
+#             response = ask_me(question, retriever, llm)
+#             st.write(f"Answer: {response}")
+#     else:
+#         st.warning("Please upload a dataset to proceed.")
+
+
+# def main():
+#     st.sidebar.title("Navigation")
+#     options = st.sidebar.radio(
+#         "Go to",
+#         [
+#             "Upload",
+#             "Preview",
+#             "Data Cleaning",
+#             "Modify Column Names",
+#             "General Data Statistics",
+#             "Describe",
+#             "Info",
+#             "Handle Categorical",
+#             "Missing Values",
+#             "Handle Duplicates",
+#             "Handle Outliers",
+#             "Download",
+#             "RAG Chatbot"
+#         ],
+#         key="unique_navigation_key",
+#     )
+
+#     if options == "Upload":
+#         upload_data()
+#     elif options == "Preview":
+#         preview_data()
+#     elif options == "Data Cleaning":
+#         data_cleaning()
+#     elif options == "Modify Column Names":
+#         modify_column_names()
+#     elif options == "General Data Statistics":
+#         show_general_data_statistics()
+#     elif options == "Describe":
+#         describe_data()
+#     elif options == "Info":
+#         info_data()
+#     elif options == "Handle Categorical":
+#         handle_categorical_values()
+#     elif options == "Missing Values":
+#         missing_values()
+#     elif options == "Handle Duplicates":
+#         handle_duplicates()
+#     elif options == "Handle Outliers":
+#         handle_outliers()
+#     elif options == "Download":
+#         download_data()
+#     elif options == "RAG Chatbot":
+#         rag_chatbot()
+
+#     else:
+#         st.warning("Please upload a dataset first.")
+
+
+# if __name__ == "__main__":
+#     main()