Update app.py

app.py

@@ -2,11 +2,12 @@ import os
 import gradio as gr
 import requests
 
-# Use Mistral via Hugging Face Inference API (requires token)
-API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1"
-headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_TOKEN')}"}
+# 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"
 
-# Example prompts
+# Suggested test prompts
 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.",
@@ -15,62 +16,51 @@ preset_prompts = [
     "I’m happy for her, but I wish I had that too.",
 ]
 
-# Call Mistral Inference API with error handling
-def call_model(prompt):
-    payload = {"inputs": prompt}
-    try:
-        response = requests.post(API_URL, headers=headers, json=payload, timeout=45)
-
-        print("Status:", response.status_code)
-        print("Raw:", response.text)
-
-        if response.status_code != 200:
-            return f"⚠️ Error: API returned {response.status_code} - model may be loading or access may be restricted."
+# Call the Hugging Face router endpoint with Mistral
+def call_mistral_chat(messages):
+    payload = {
+        "model": MODEL,
+        "messages": messages
+    }
 
+    try:
+        response = requests.post(API_URL, headers=HEADERS, json=payload, timeout=45)
+        response.raise_for_status()
         data = response.json()
-        if isinstance(data, list) and "generated_text" in data[0]:
-            return data[0]["generated_text"]
-        elif isinstance(data, dict) and "error" in data:
-            return f"⚠️ API Error: {data['error']}"
-        else:
-            return "⚠️ Unexpected response format from model."
 
+        return data["choices"][0]["message"]["content"]
     except Exception as e:
-        return f"⚠️ Request failed: {str(e)}"
+        return f"⚠️ Error: {str(e)}"
 
-# Emotion analysis logic
-def emotion_annotator(text):
-    prompt1 = f"""You are an expert in human emotions. Based on the sentence below, list all possible emotions the person might be feeling.
+# 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)
 
-Sentence: "{text}"
-Emotions:"""
-    candidates = call_model(prompt1)
+    # 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)
 
-    prompt2 = f"""Given the sentence "{text}" and the following candidate emotions: {candidates}, pick the most likely emotion and explain why.
-
-Format:
-Most likely emotion: <emotion>
-Reason: <why>"""
-    final = call_model(prompt2)
-
-    return candidates.strip(), final.strip()
+    return candidate_emotions.strip(), final_emotion.strip()
 
 # Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 Emotion Annotator AI (Mistral 7B via Hugging Face API)")
-    gr.Markdown("Disambiguates mixed emotions using `mistralai/Mistral-7B-Instruct-v0.1`. Token required.")
+    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.")
 
     with gr.Row():
-        text_input = gr.Textbox(label="Enter your 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.", lines=2)
         dropdown = gr.Dropdown(preset_prompts, label="💬 Choose an example")
-
     run_button = gr.Button("Submit")
 
     with gr.Row():
         candidate_output = gr.Textbox(label="🧠 Candidate Emotions")
         final_output = gr.Textbox(label="🎯 Most Likely Emotion + Explanation")
 
-    # Auto-fill from dropdown
+    # Auto-fill textbox from dropdown
     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])