Update app.py

app.py

@@ -1,51 +1,49 @@
 import os
 import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from huggingface_hub import login
-
-# Authenticate with Hugging Face using your secret token
-login(token=os.getenv("HUGGINGFACE_TOKEN"))
-
-MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"
-
-# Load model and tokenizer using token
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_auth_token=True)
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    device_map="auto",  # Automatically chooses CPU or GPU
-    use_auth_token=True
-)
-
-# Create a pipeline
-generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-# Define emotion annotation pipeline
+import requests
+
+# Set up Hugging Face Inference API
+API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1"
+headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_TOKEN')}"}
+
+def call_model(prompt):
+    payload = {"inputs": prompt}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    
+    try:
+        return response.json()[0]["generated_text"]
+    except (KeyError, IndexError, TypeError):
+        return "⚠️ Error: Could not generate a valid response. Check API status or token."
+
+# Emotion disambiguation pipeline
 def emotion_annotator(text):
-    # Step 1: Emotion candidates
-    prompt1 = f"""List all possible emotions the person might be feeling based on this sentence:
-"{text}"
+    # Step 1: List candidate emotions
+    prompt1 = f"""You are an expert in human emotions. Based on the sentence below, list all possible emotions the person might be feeling.
+
+Sentence: "{text}"
 Emotions:"""
-    emotions_output = generator(prompt1, max_new_tokens=100)[0]["generated_text"]
-    emotion_list = emotions_output.split("Emotions:")[-1].strip()
+    candidates = call_model(prompt1)
+
+    # Step 2: Choose most likely one
+    prompt2 = f"""You are an emotion disambiguation expert. Given the sentence "{text}" and the following candidate emotions: {candidates}, pick the most likely one and explain why.
 
-    # Step 2: Disambiguation
-    prompt2 = f"""Now from this list of emotions: {emotion_list}, pick the most likely emotion the person is feeling and explain why. Text: "{text}" """
-    disambiguation = generator(prompt2, max_new_tokens=150)[0]["generated_text"]
+Format:
+Most likely emotion: <emotion>
+Reason: <why you chose it>"""
+    final = call_model(prompt2)
 
-    return emotion_list, disambiguation
+    return candidates.strip(), final.strip()
 
-# Gradio interface
+# Gradio UI
 demo = gr.Interface(
     fn=emotion_annotator,
-    inputs=gr.Textbox(lines=2, placeholder="Type a journal entry or emotional sentence..."),
+    inputs=gr.Textbox(lines=2, placeholder="e.g., I’m proud but I feel like I let them down."),
     outputs=[
         gr.Textbox(label="Candidate Emotions"),
         gr.Textbox(label="Most Likely Emotion + Explanation")
     ],
-    title="🧠 Emotion Annotator AI",
-    description="Disambiguate mixed feelings in text using Mistral 7B Instruct model via Hugging Face token authentication"
+    title="🧠 Emotion Annotator AI (via Hugging Face Inference API)",
+    description="Uses Mistral 7B Instruct on Hugging Face to analyze and disambiguate mixed emotions from text input. No model download needed!"
 )
 
 demo.launch()