Fix: update dependencies,deepseek

app.py

@@ -1,50 +1,32 @@
 import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 from peft import PeftModel
 import torch
 
 @st.cache_resource
 def load_model():
-    base_model = "Qwen/Qwen3-0.6B"
-    adapter_path = "faizabenatmane/Qwen-3-0.6B"
+    base_model = "deepseek-ai/deepseek-coder-5.7b-base"
+    adapter_path = "faizabenatmane/deepseek-coder-5.7bmqa-finetuned"
 
     tokenizer = AutoTokenizer.from_pretrained(base_model)
-
-    base = AutoModelForSequenceClassification.from_pretrained(
-        base_model,
-        num_labels=2,
-        device_map="cpu"
-    )
+    base = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")
 
     model = PeftModel.from_pretrained(base, adapter_path)
     model = model.merge_and_unload()
 
-    pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
     return pipe
 
-classifier = load_model()
-
-# Streamlit UI
-st.title("📰 Fake News Detection")
-text = st.text_area("Enter a news statement or claim:", height=200)
-
-if st.button("Check"):
-    with st.spinner("Analyzing..."):
-        result = classifier(text)[0]
-        label = result['label']
-        score = result['score']
+generator = load_model()
 
-        st.write("🔎 Raw label:", label)  # Debug print
+st.title("🧠 DeepSeek QA (Generation)")
+text = st.text_area("Ask a coding or general question:", height=200)
 
-        if label == "LABEL_1":
-            verdict = "Real"
-            emoji = "✅"
-        elif label == "LABEL_0":
-            verdict = "Fake"
-            emoji = "❌"
-        else:
-            verdict = f"Unclear ({label})"
-            emoji = "🤔"
+if st.button("Generate Answer"):
+    with st.spinner("Generating..."):
+        prompt = f"Question: {text}\nAnswer:"
+        output = generator(prompt, max_new_tokens=100, do_sample=False)[0]["generated_text"]
+        answer = output.split("Answer:")[-1].strip()
 
-        st.subheader("Prediction")
-        st.success(f"{emoji} The statement is likely: **{verdict}** (confidence: {score:.2f})")
+        st.subheader("Generated Answer")
+        st.success(answer)

requirements.txt

@@ -1,4 +1,4 @@
-transformers>=4.38.0
-torch>=2.1.0
-peft
-streamlit
+transformers==4.40.1
+torch==2.2.2
+peft==0.9.0
+streamlit==1.34.0