Update app.py

app.py

@@ -1,13 +1,14 @@
 import os
 import gradio as gr
-import requests
+from huggingface_hub import InferenceClient
 
-# Hugging Face Chat Completion Endpoint
-API_URL = "https://router.huggingface.co/novita/v3/openai/chat/completions"
-HEADERS = {"Authorization": f"Bearer {os.environ['HF_TOKEN']}"}
-MODEL = "mistralai/Mistral-7B-Instruct-v0.1"
+# Load Hugging Face token from secret
+client = InferenceClient(
+    provider="nscale",  # You can change to 'openrouter' or 'novita' if needed
+    api_key=os.environ["HF_TOKEN"],
+)
 
-# Suggested test prompts
+# Test prompt list
 preset_prompts = [
     "I finally got the promotion, but I feel guilty because my best friend got laid off.",
     "Moving to a new city is exciting, but leaving my family breaks my heart.",
@@ -16,43 +17,46 @@ preset_prompts = [
     "I’m happy for her, but I wish I had that too.",
 ]
 
-# Call the Hugging Face router endpoint with Mistral
-def call_mistral_chat(messages):
-    payload = {
-        "model": MODEL,
-        "messages": messages
-    }
-
+# Core generation logic using chat completion
+def call_llama(messages):
     try:
-        response = requests.post(API_URL, headers=HEADERS, json=payload, timeout=45)
-        response.raise_for_status()
-        data = response.json()
-
-        return data["choices"][0]["message"]["content"]
+        completion = client.chat.completions.create(
+            model="meta-llama/Llama-3.1-8B-Instruct",
+            messages=messages,
+        )
+        return completion.choices[0].message.content.strip()
     except Exception as e:
         return f"⚠️ Error: {str(e)}"
 
-# Emotion annotation logic
-def emotion_annotator(user_text):
-    # Step 1: Generate candidate emotions
-    prompt1 = f"You are an emotion analysis expert. List all possible emotions the person might be feeling in this sentence:\n\n\"{user_text}\"\n\nAnswer with just the emotion names."
-    messages1 = [{"role": "user", "content": prompt1}]
-    candidate_emotions = call_mistral_chat(messages1)
+# Emotion pipeline
+def emotion_annotator(text):
+    # Step 1: List candidate emotions
+    msg1 = [
+        {
+            "role": "user",
+            "content": f'List all possible emotions the person might be feeling in this sentence:\n"{text}"\nJust give comma-separated emotion names.'
+        }
+    ]
+    candidates = call_llama(msg1)
 
-    # Step 2: Disambiguate to most likely emotion
-    prompt2 = f"""Now from this list of emotions: {candidate_emotions}, pick the most likely one the person is feeling and explain why. Sentence: "{user_text}"\n\nFormat:\nMost likely emotion: <emotion>\nReason: <why>"""
-    messages2 = [{"role": "user", "content": prompt2}]
-    final_emotion = call_mistral_chat(messages2)
+    # Step 2: Choose most likely emotion with reason
+    msg2 = [
+        {
+            "role": "user",
+            "content": f'From these emotions: {candidates}, which is most likely the dominant one in the sentence "{text}"? Explain why briefly.\nFormat:\nMost likely emotion: <emotion>\nReason: <why>'
+        }
+    ]
+    final = call_llama(msg2)
 
-    return candidate_emotions.strip(), final_emotion.strip()
+    return candidates, final
 
 # Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 Emotion Annotator AI (Powered by Mistral 7B via HF Chat API)")
-    gr.Markdown("Disambiguates complex emotions using `mistralai/Mistral-7B-Instruct-v0.1` through Hugging Face's Chat Completion endpoint.")
+    gr.Markdown("## 🧠 Emotion Annotator (LLaMA 3.1 via Hugging Face Chat API)")
+    gr.Markdown("Powered by `meta-llama/Llama-3.1-8B-Instruct`, served using the InferenceClient chat interface.")
 
     with gr.Row():
-        text_input = gr.Textbox(label="📝 Input Sentence", placeholder="e.g., I’m proud but I feel like I let them down.", lines=2)
+        text_input = gr.Textbox(label="✏️ Input Sentence", placeholder="e.g., I’m proud but I feel like I let them down.")
         dropdown = gr.Dropdown(preset_prompts, label="💬 Choose an example")
     run_button = gr.Button("Submit")
 
@@ -60,7 +64,7 @@ with gr.Blocks() as demo:
         candidate_output = gr.Textbox(label="🧠 Candidate Emotions")
         final_output = gr.Textbox(label="🎯 Most Likely Emotion + Explanation")
 
-    # Auto-fill textbox from dropdown
+    # Dropdown autofill
     dropdown.change(fn=lambda x: x, inputs=dropdown, outputs=text_input)
     run_button.click(fn=emotion_annotator, inputs=text_input, outputs=[candidate_output, final_output])