Update app.py

app.py

@@ -1,3 +1,5 @@
+from huggingface_hub import HfApi
+import tempfile
 import streamlit as st
 import pandas as pd
 import io, os
@@ -74,6 +76,10 @@ reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
 
 vectorstore_global = None
 
+# Initialize feedback list
+if "feedback_log" not in st.session_state:
+    st.session_state.feedback_log = []
+
 def load_environment():
     load_dotenv()
     genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
@@ -239,6 +245,30 @@ def handle_user_query(query):
     else:
         context = faiss_search_with_keywords(query)
     return self_reasoning(query, context)
+
+def save_feedback_to_huggingface():
+    if not st.session_state.feedback_log:
+        return  # No feedback yet
+
+    feedback_df = pd.DataFrame(st.session_state.feedback_log)
+    now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"feedback_{now}.csv"
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        filepath = os.path.join(tmpdir, filename)
+        feedback_df.to_csv(filepath, index=False)
+
+        api = HfApi(token=os.environ["HF_TOKEN"])
+        api.upload_file(
+            path_or_fileobj=filepath,
+            path_in_repo=filename,
+            repo_id="ZarinT/chatbot-feedback",  
+            repo_type="dataset"
+        )
+
+    # Clear feedback log after upload
+    st.session_state.feedback_log.clear()
+
     
 def main():
     load_environment()
@@ -262,39 +292,36 @@ def main():
 
     user_question = st.text_input("Ask your question:", key="user_input")
 
-    if st.button("Submit") and user_question:
+    if st.session_state.chat_ready and user_question:
         set_global_vectorstore(st.session_state.vectorstore)
         response = handle_user_query(user_question)
-        st.session_state.chat_history.append({"user": user_question, "bot": response})
-        
-
-    if "feedback_log" not in st.session_state:
-        st.session_state.feedback_log = []
-
-    for i, chat in enumerate(st.session_state.chat_history):
-        st.write(f"**You:** {chat['user']}")
-        st.write(f"**Bot:** {chat['bot']}")
 
-        rating = st.radio(
-            "How would you rate this response?",
-            options=["1", "2", "3", "4", "5"],
-            key=f"rating_{i}",
-            horizontal=True
-        )
+        st.session_state.chat_history.append({"user": user_question, "bot": response})
 
-    
-        st.session_state.feedback_log.append({
-            "question": chat["user"],
-            "answer": chat["bot"],
-            "rating": rating
-        })
-
-
-   
-    if st.session_state.feedback_log:
-        feedback_df = pd.DataFrame(st.session_state.feedback_log)
-        current_date = datetime.now().strftime("%Y%m%d_%H%M%S")
-        feedback_df.to_csv(f"feedback_{current_date}.csv", index=False)
+        # After bot responds, ask for feedback
+        with st.form(key=f"feedback_form_{len(st.session_state.chat_history)}"):
+            rating = st.radio(
+                "Rate this response:",
+                options=["1", "2", "3", "4", "5"],
+                key=f"rating_{len(st.session_state.chat_history)}",
+                horizontal=True
+            )
+            submitted = st.form_submit_button("Submit Rating")
+            if submitted:
+                feedback = {
+                    "question": user_question,
+                    "response": response,
+                    "rating": rating,
+                    "timestamp": datetime.datetime.now().isoformat()
+                }
+                st.session_state.feedback_log.append(feedback)
+
+                # If 5 feedbacks collected, auto upload to Hugging Face
+                if len(st.session_state.feedback_log) >= 5:
+                    save_feedback_to_huggingface()
+
+        # Clear input box automatically
+        st.experimental_rerun()
 
 if __name__ == "__main__":
     load_environment()