Update app.py

app.py

@@ -1,64 +1,46 @@
-import streamlit as st
-from transformers import pipeline
-from datasets import Dataset, load_dataset
-from huggingface_hub import HfApi, HfFolder
-import os
-
-# 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(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": []})
+import json
+from datetime import datetime
+from pathlib import Path
+from uuid import uuid4
 
-# App title
-st.title("Sentiment Bot with Feedback")
-
-# Sentiment analysis pipeline
-sentiment_pipeline = pipeline("sentiment-analysis", framework="pt")
-
-# Input text area
-user_input = st.text_area("Enter the Review")
+import streamlit as st
 
-if st.button("Analyze"):
-    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?")
-        
-        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}")
+from huggingface_hub import CommitScheduler
+
+# Directory for JSON dataset
+JSON_DATASET_DIR = Path("json_dataset")
+JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
+
+JSON_DATASET_PATH = JSON_DATASET_DIR / f"train-{uuid4()}.json"
+
+scheduler = CommitScheduler(
+    repo_id="example-space-to-dataset-json",
+    repo_type="dataset",
+    folder_path=JSON_DATASET_DIR,
+    path_in_repo="data",
+)
+
+# Function to greet the user
+def greet(name: str) -> str:
+    return "Hello " + name + "!"
+
+# Function to save data as JSON
+def save_json(name: str, greetings: str) -> None:
+    with scheduler.lock:
+        with JSON_DATASET_PATH.open("a") as f:
+            json.dump({"name": name, "greetings": greetings, "datetime": datetime.now().isoformat()}, f)
+            f.write("\n")
+
+# Streamlit UI
+st.title("Greeting App")
+
+# Input and Output
+name = st.text_input("Enter your name:")
+if st.button("Greet"):
+    if name:
+        greeting = greet(name)
+        st.success(greeting)
+        save_json(name, greeting)
+        st.write("Greeting saved successfully!")
     else:
-        st.error("Please enter a review before analyzing.")
-
-# Display feedback collected so far
-st.subheader("Feedback Collected")
-st.dataframe(feedback_dataset.to_pandas())
+        st.error("Please enter your name.")