Pushed HF files

.github/workflows/sync_to_hub.yml

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+name: Sync to Hugging Face Hub
+
+on:
+  push:
+    branches: [main]
+
+    # This workflow runs whenever you push to the main branch
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push https://shri171981:$HF_TOKEN@huggingface.co/spaces/shri171981/genai_sentiment_classifier main

README.md

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+---
+title: Genai Sentiment Classifier
+emoji: 🐠
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 6.0.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# 1. SETUP MODEL
+MODEL_ID = "shri171981/genai_project_deployment"
+
+# 2. DEFINE LABELS
+LABELS = ["Anger", "Fear", "Joy", "Sadness", "Surprise"]
+
+# 3. LOAD PIPELINE
+pipe = pipeline("text-classification", model=MODEL_ID, top_k=None)
+
+def predict(text):
+    # Run inference
+    results = pipe(text)[0]
+    
+    # Create a dictionary of { "Joy": 0.95, "Anger": 0.02 } for Gradio
+    output_scores = {}
+    for result in results:
+        # Extract the ID (e.g., "LABEL_0" -> 0)
+        label_id = int(result['label'].split('_')[-1])
+        
+        # Map ID to the text name (e.g., 0 -> "Anger")
+        label_name = LABELS[label_id]
+        
+        # Add to dict
+        output_scores[label_name] = result['score']
+    
+    return output_scores
+
+# 4. UI
+theme = gr.themes.Soft(
+    primary_hue="orange",
+    secondary_hue="slate",
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Emotion Classifier
+        ### Analyze the sentiment of your text across 5 different emotions.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Input Text", 
+                placeholder="Type something emotional here...",
+                lines=3
+            )
+            submit_btn = gr.Button("Analyze Emotion", variant="primary")
+            
+            gr.Examples(
+                examples=[
+                    ["I can't believe you would betray me like this!"], # Anger
+                    ["I heard a strange noise outside and I'm scared to look."], # Fear
+                    ["I finally got the promotion! This is the best day ever!"], # Joy
+                    ["I feel so lonely and empty inside."], # Sadness
+                    ["Wow! I never expected a surprise party!"] # Surprise
+                ],
+                inputs=input_text
+            )
+            
+        with gr.Column():
+            # This component creates the bar chart automatically
+            output_chart = gr.Label(label="Emotion Confidence Scores", num_top_classes=5)
+
+    # Link the button to the function
+    submit_btn.click(fn=predict, inputs=input_text, outputs=output_chart)
+
+# 5. LAUNCH
+if __name__ == "__main__":
+    demo.launch()

gitattributes

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requirements.txt

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+transformers
+torch
+sentencepiece
+protobuf
+accelerate