Update app.py

app.py

@@ -1,11 +1,22 @@
 import streamlit as st
 from transformers import pipeline
 from datasets import Dataset, load_dataset
+from huggingface_hub import HfApi, HfFolder
+import os
 
-# Initialize or load a dataset from HuggingFace (requires an existing dataset repository)
+# Set your Hugging Face API token
+HF_TOKEN = os.getenv("HF_token")  # Ensure this environment variable is set securely
+
+# Initialize Hugging Face API
+api = HfApi()
+api.set_access_token(HF_TOKEN)
+
+# Initialize or load the dataset from Hugging Face
+dataset_repo = "utsav2001/Feedback"
 try:
-    feedback_dataset = load_dataset("your_username/feedback_dataset", split="train")
-except:
+    feedback_dataset = load_dataset(dataset_repo, split="train")
+except Exception as e:
+    st.warning(f"Could not load dataset: {e}. Initializing a new dataset.")
     feedback_dataset = Dataset.from_dict({"input": [], "response": [], "rating": [], "feedback": []})
 
 # App title
@@ -18,28 +29,35 @@ sentiment_pipeline = pipeline("sentiment-analysis", framework="pt")
 user_input = st.text_area("Enter the Review")
 
 if st.button("Analyze"):
-    # Analyze sentiment
-    result = sentiment_pipeline(user_input)
-    sentiment = result[0]['label']
-    confidence = result[0]['score']
-    
-    # Display sentiment and confidence
-    st.write("Sentiment:", sentiment)
-    st.write("Confidence Score:", confidence)
-    
-    # Collect user feedback
-    st.subheader("Provide Feedback")
-    rating = st.slider("Rate the response (out of 4):", 1, 4)
-    feedback = st.text_area("Any additional feedback?")
-    
-    if st.button("Submit Feedback"):
-        # Save feedback to the dataset
-        new_data = {"input": [user_input], "response": [sentiment], "rating": [rating], "feedback": [feedback]}
-        feedback_dataset = feedback_dataset.add_item(new_data)
-        st.success("Feedback submitted!")
+    if user_input.strip():
+        # Analyze sentiment
+        result = sentiment_pipeline(user_input)
+        sentiment = result[0]['label']
+        confidence = result[0]['score']
+        
+        # Display sentiment and confidence
+        st.write("Sentiment:", sentiment)
+        st.write("Confidence Score:", confidence)
+        
+        # Collect user feedback
+        st.subheader("Provide Feedback")
+        rating = st.slider("Rate the response (out of 4):", 1, 4)
+        feedback = st.text_area("Any additional feedback?")
         
-        # Optionally, save the dataset locally or to HuggingFace
-        feedback_dataset.push_to_hub("Utsav2001/Feedback", token=HF_token)
+        if st.button("Submit Feedback"):
+            # Save feedback to the dataset
+            new_data = {"input": user_input, "response": sentiment, "rating": rating, "feedback": feedback}
+            feedback_dataset = feedback_dataset.add_item(new_data)
+            st.success("Feedback submitted!")
+            
+            # Push the updated dataset to Hugging Face Hub
+            try:
+                feedback_dataset.push_to_hub(dataset_repo, token=HF_TOKEN)
+                st.info("Feedback dataset updated on Hugging Face Hub.")
+            except Exception as e:
+                st.error(f"Failed to push dataset to Hub: {e}")
+    else:
+        st.error("Please enter a review before analyzing.")
 
 # Display feedback collected so far
 st.subheader("Feedback Collected")