Update app.py

app.py

@@ -1,105 +1,18 @@
-import json
-from datetime import datetime
-from pathlib import Path
-from uuid import uuid4
-import os
-
 import gradio as gr
-from huggingface_hub import CommitScheduler
 from transformers import pipeline
 
-# Setup the directory for saving data
-JSON_DATASET_DIR = Path("feedback_dataset")
-JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
-
-# Define a unique file name
-JSON_DATASET_PATH = JSON_DATASET_DIR / f"feedback-{uuid4()}.json"
-
-# Scheduler configuration for your dataset repo
-scheduler = CommitScheduler(
-    repo_id="Utsav2001/Feedback",  # Your dataset repo
-    repo_type="dataset",
-    folder_path=JSON_DATASET_DIR,  # Local directory to sync
-    path_in_repo="data",  # Path in the dataset repository
-    token=os.getenv('hf_write')
-)
-
-# Initialize Sentiment Analysis Pipeline
-try:
-    sentiment_pipeline = pipeline("sentiment-analysis")
-except Exception as e:
-    raise RuntimeError(f"Failed to load sentiment-analysis pipeline: {e}")
-
-# Function to analyze sentiment
-def analyze_sentiment(user_input: str):
-    try:
-        result = sentiment_pipeline(user_input)[0]
-        sentiment = result['label']
-        confidence = result['score']
-        return f"Sentiment: {sentiment}, Confidence: {confidence:.2f}", sentiment, confidence
-    except Exception as e:
-        return f"Error analyzing sentiment: {e}", None, None
-
-# Function to save data
-def save_feedback(user_input: str, sentiment: str, confidence: float, user_feedback: str) -> str:
-    try:
-        with scheduler.lock:
-            with JSON_DATASET_PATH.open("a") as f:
-                json.dump(
-                    {
-                        "review": user_input,
-                        "sentiment": sentiment,
-                        "confidence": confidence,
-                        "user_feedback": user_feedback,
-                        "datetime": datetime.now().isoformat(),
-                    },
-                    f,
-                )
-                f.write("\n")
-        # Push the changes to Hugging Face Hub
-        scheduler.push_to_hub()
-        return "Feedback saved successfully!"
-    except Exception as e:
-        return f"Error saving feedback: {e}"
-
-# Gradio Interface
-with gr.Blocks() as demo:
-    with gr.Column():
-        gr.Markdown("### Sentiment Analysis with User Feedback")
-        
-        with gr.Row():
-            user_input = gr.Textbox(label="Enter Your Review", placeholder="Type your review here...")
-            analyze_button = gr.Button("Analyze")
-        
-        analysis_output = gr.Textbox(label="Analysis Result", interactive=False)
-        
-        feedback_section = gr.Column(visible=False)  # Initially hidden
-        with feedback_section:
-            feedback_input = gr.Textbox(label="Your Feedback on the Model's Response", placeholder="Type your feedback here...")
-            feedback_button = gr.Button("Submit Feedback")
-        
-        feedback_status = gr.Textbox(label="Feedback Status", interactive=False)
-
-    # Button Logic
-    def analyze_and_show_feedback(user_input):
-        result_message, sentiment, confidence = analyze_sentiment(user_input)
-        return result_message, gr.update(visible=True), user_input, sentiment, confidence
-
-    def submit_feedback(user_input, sentiment, confidence, user_feedback):
-        if not user_feedback:
-            return "Please provide feedback before submitting."
-        return save_feedback(user_input, sentiment, confidence, user_feedback)
-
-    # Connect button actions
-    analyze_button.click(
-        fn=analyze_and_show_feedback,
-        inputs=[user_input],
-        outputs=[analysis_output, feedback_section, user_input, feedback_section, feedback_section]
-    )
-    feedback_button.click(
-        fn=submit_feedback,
-        inputs=[user_input, analysis_output, feedback_status, user_input],
-        outputs=feedback_status
-    )
-
-demo.launch()
+# Load the sentiment analysis pipeline from Hugging Face
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+def sentiment_analysis(message, history):
+    # Perform sentiment analysis on the user's message
+    result = sentiment_pipeline(message)[0]
+    label = result["label"]
+    score = result["score"]
+    return f"Sentiment: {label}, Confidence: {score:.2f}"
+
+# Create a Gradio ChatInterface with the sentiment analysis function
+gr.ChatInterface(
+    fn=sentiment_analysis, 
+    type="messages"
+).launch()