Update app.py

app.py

@@ -13,13 +13,12 @@ def load_style_samples():
 
 @st.cache_resource(show_spinner=False)
 def load_pipeline():
-    # Replace model ID with a more powerful public LLM if you want
-    model_id = "google/flan-t5-base"  # For English text generation
+    # Choose a lightweight, CPU-friendly model for Spaces
+    model_id = "google/flan-t5-base"
+    # IMPORTANT: Do NOT pass device_map here to avoid needing accelerate
     gen_pipe = pipeline(
-        "text2text-generation",
-        model=model_id,
-        device_map="auto",
-        trust_remote_code=True
+        task="text2text-generation",
+        model=model_id
     )
     return gen_pipe
 
@@ -28,7 +27,7 @@ style_samples = load_style_samples()
 
 st.set_page_config(page_title="LinkedIn Post Generator", layout="centered")
 st.title("🔗 LinkedIn Post Generator (Hugging Face)")
-st.write("Generate LinkedIn posts in your writing style.")
+st.write("Generate LinkedIn posts in your style using a compact open model.")
 
 with st.form("gen_form"):
     topic = st.text_input("Post Topic", "Generative AI for Business")
@@ -36,14 +35,16 @@ with st.form("gen_form"):
     audience = st.text_input("Audience", "Startup founders")
     length = st.slider("Length (approx words)", 30, 400, 120, 10)
     use_sample = st.selectbox(
-        "Style Sample (optional)", ["None"] + [f"Sample {i+1}" for i in range(len(style_samples))])
+        "Style Sample (optional)",
+        ["None"] + [f"Sample {i+1}" for i in range(len(style_samples))]
+    )
     custom_style = st.text_area("Or paste your own style sample (optional)")
     submitted = st.form_submit_button("Generate Post")
 
 prompt_style = ""
 if use_sample != "None":
-    sample_idx = int(use_sample.split()[1]) - 1
-    prompt_style += f"Sample style: {style_samples[sample_idx]}\n"
+    idx = int(use_sample.split()[1]) - 1
+    prompt_style += f"Sample style: {style_samples[idx]}\n"
 if custom_style.strip():
     prompt_style += f"User style: {custom_style}\n"
 
@@ -56,11 +57,19 @@ prompt = (
 
 if submitted:
     if not topic.strip():
-        st.warning("Please enter a topic before generating.")
+        st.warning("Please enter a topic.")
     else:
         with st.spinner("Generating..."):
             try:
-                result = pipe(prompt, max_new_tokens=length + 32)[0]["generated_text"].strip()
+                # flan-t5 uses text2text; pipeline returns list of dicts
+                outputs = pipe(prompt, max_new_tokens=length + 48)
+                # Support both list and dict return shapes defensively
+                if isinstance(outputs, list) and len(outputs) and "generated_text" in outputs[0]:
+                    result = outputs[0]["generated_text"].strip()
+                elif isinstance(outputs, dict) and "generated_text" in outputs:
+                    result = outputs["generated_text"].strip()
+                else:
+                    result = str(outputs)
                 st.success("Here's your LinkedIn post:")
                 st.write(result)
                 st.download_button("Download post as .txt", result, file_name="linkedin_post.txt")
@@ -68,13 +77,15 @@ if submitted:
                 st.error(f"Error generating post: {e}")
 
 st.markdown("---")
-st.write("Upload a new list of style samples (JSON array of strings, replaces existing):")
+st.write("Upload a JSON array of style samples (strings). This overwrites existing samples.")
 file = st.file_uploader("Upload style_samples.json", type=["json"])
 if file:
     try:
         data = json.load(file)
+        if not isinstance(data, list) or not all(isinstance(x, str) for x in data):
+            raise ValueError("JSON must be a list of strings.")
         with open(STYLE_SAMPLES_FILE, "w") as f:
             json.dump(data, f)
-        st.success(f"Uploaded and saved {len(data)} samples. Reload Space to use.")
+        st.success(f"Saved {len(data)} samples. Reload the app to use them.")
     except Exception as e:
         st.error(f"Upload failed: {e}")