Update app.py

app.py

@@ -2,23 +2,35 @@ import os
 import numpy as np
 import pandas as pd
 import streamlit as st
-from huggingface_hub import login
-from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForCausalLM
 from keybert import KeyBERT
-from openai import AzureOpenAI  # new
 
-# ─── Azure OpenAI Client ─────────────────────────────────────────────────────
-openai_client = AzureOpenAI(
-  api_key = "fbca46bfd8814334be46a2e5c323904c", # use your key here
-  api_version = "2023-05-15", # apparently HKUST uses a deprecated version
-  azure_endpoint = "https://hkust.azure-api.net" # per HKUST instructions
+# ─── DeepSeek Model Client ────────────────────────────────────────────────────
+# Option 1: High-level helper pipeline for chat-like generation
+pipe = pipeline(
+    "text-generation",
+    model="deepseek-ai/DeepSeek-R1",
+    trust_remote_code=True
 )
 
+# Option 2: Direct model & tokenizer instantiation (alternative)
+# tokenizer_ds = AutoTokenizer.from_pretrained(
+#     "deepseek-ai/DeepSeek-R1",
+#     trust_remote_code=True
+# )
+# model_ds = AutoModelForCausalLM.from_pretrained(
+#     "deepseek-ai/DeepSeek-R1",
+#     trust_remote_code=True
+# )
+
 @st.cache_resource
 def load_sentiment_pipeline():
     model_name = "mayf/amazon_reviews_bert_ft"
     tok = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
-    mdl = AutoModelForSequenceClassification.from_pretrained(model_name, use_auth_token=True)
+    mdl = AutoModelForSequenceClassification.from_pretrained(
+        model_name,
+        use_auth_token=True
+    )
     return pipeline(
         "sentiment-analysis",
         model=mdl,
@@ -61,7 +73,6 @@ def main():
     # Run sentiment analysis
     progress.text("Analyzing sentiment...")
     raw_scores = sentiment_pipeline(review)[0]
-    # Map labels
     sentiment_results = {LABEL_MAP[item['label']]: float(item['score']) for item in raw_scores}
     progress.progress(40)
 
@@ -108,21 +119,22 @@ Tasks:
 2. Recommendations: Three separate paragraphs with actionable suggestions (max 30 words each).
 """
 
-    response = openai_client.chat.completions.create(
-        model="gpt-35-turbo",
-        messages=[
-            {"role": "system", "content": "You are a product-feedback analyst."},
-            {"role": "user", "content": prompt}
-        ],
-        temperature=0.7,
-        max_tokens=200
-    )
-    gpt_reply = response.choices[0].message.content.strip()
-    st.markdown(gpt_reply)
+    # Use the high-level pipeline for generation
+    chat_input = [
+        {"role": "system", "content": "You are a product-feedback analyst."},
+        {"role": "user", "content": prompt}
+    ]
+    gen_output = pipe(chat_input)
+    gpt_reply = gen_output[0]['generated_text']
+
+    # Alternative: direct model invocation
+    # inputs = tokenizer_ds(prompt, return_tensors="pt")
+    # outputs = model_ds.generate(**inputs, max_new_tokens=200)
+    # gpt_reply = tokenizer_ds.decode(outputs[0], skip_special_tokens=True)
 
-    # Complete
+    st.markdown(gpt_reply)
     progress.progress(100)
     progress.text("Done!")
 
 if __name__ == "__main__":
-    main()
+    main()