Update app.py

app.py

@@ -1,87 +1,79 @@
 import streamlit as st
 import json
-from transformers import pipeline
-from huggingface_hub import login
 import os
+from transformers import pipeline
 
-# Get API key from Hugging Face secrets (set in repo settings)
-HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
-if HUGGINGFACE_TOKEN:
-    login(token=HUGGINGFACE_TOKEN)
-
-# Load example style samples from local JSON
-SAMPLE_FILE = "style_samples.json"
-if os.path.exists(SAMPLE_FILE):
-    with open(SAMPLE_FILE, "r") as f:
-        style_samples = json.load(f)
-else:
-    style_samples = []
+def load_style_samples(filename="style_samples.json"):
+    if os.path.exists(filename):
+        with open(filename, "r") as f:
+            return json.load(f)
+    return []
 
-# Cache the pipeline for fast reload
 @st.cache_resource(show_spinner=False)
-def load_pipeline():
-    # Try loading a fast, modern model — can swap to desired model (Llama, Mixtral...)
-    # For example: "meta-llama/Llama-2-7b-chat-hf" or any open model
-    model_id = "google/flan-t5-base" # Replace with your favorite
-    pipe = pipeline("text2text-generation", model=model_id)
-    return pipe
+def load_text_gen_pipeline():
+    model_id = "google/flan-t5-base"  # Change to a larger or different supported model as needed
+    return pipeline(
+        "text2text-generation", 
+        model=model_id, 
+        device_map="auto",
+        trust_remote_code=True
+    )
 
-pipe = load_pipeline()
+# Load model and style samples
+pipe = load_text_gen_pipeline()
+style_samples = load_style_samples()
 
-# UI
 st.set_page_config(page_title="LinkedIn Post Generator", layout="centered")
-st.title("🔗 LinkedIn Post Generator (Hugging Face)")
-st.write("Generate high-quality LinkedIn posts using GenAI. Provide a topic, select style, and go!")
+st.title("🔗 LinkedIn Post Generator (Hugging Face Space)")
+st.write("Generate LinkedIn posts with custom style and parameters.")
 
-with st.form("post_form"):
+with st.form("generate_form"):
     topic = st.text_input("Post topic", "Generative AI for Business")
     tone = st.selectbox("Tone", ["Professional", "Friendly", "Inspirational", "Technical", "Concise"])
     audience = st.text_input("Audience", "Startup founders")
-    length = st.slider("Post length (words)", 50, 500, 150, 10)
-    style_option = st.selectbox(
-        "Choose style sample",
-        ["None"] + [f"Sample {i+1}" for i in range(len(style_samples))]
-    )
-    custom_style = st.text_area("Or paste your own style example", "")
-    submit = st.form_submit_button("Generate LinkedIn Post")
+    length = st.slider("Approximate length (words)", 50, 500, 150, 10)
+    use_sample = st.selectbox(
+        "Optional: Select style sample", 
+        ["None"] + [f"Sample {i+1}" for i in range(len(style_samples))])
+    custom_example = st.text_area("Or paste your own style sample (optional)")
+    submitted = st.form_submit_button("Generate Post")
 
-prompt_examples = ""
-if style_option != "None":
-    example_idx = int(style_option.split()[1]) - 1
-    prompt_examples += f"Example style post: {style_samples[example_idx]}\n"
-if custom_style.strip():
-    prompt_examples += f"User style sample: {custom_style}\n"
+style_example_text = ""
+if use_sample != "None":
+    idx = int(use_sample.split()[1]) - 1
+    style_example_text += f"Example post style (imitate tone & voice): {style_samples[idx]}\n"
+if custom_example.strip():
+    style_example_text += f"Custom example: {custom_example}\n"
 
-# Compose the prompt
 prompt = (
-    f"Write a LinkedIn post on the topic '{topic}'.\n"
-    f"Tone: {tone}.\nAudience: {audience}.\nTarget length: {length} words.\n{prompt_examples}"
-    "Instructions: The post should engage the specified audience, use the provided style, and end with a call to action."
+    f"Write a LinkedIn post about '{topic}'.\n"
+    f"Tone: {tone}. Audience: {audience}. Target length: {length} words.\n"
+    f"{style_example_text}" 
+    "Make it engaging and end with a relevant call to action."
 )
 
-# Results area
-if submit:
+if submitted:
     if not topic.strip():
-        st.warning("Please enter a topic for your post.")
+        st.warning("Please enter a topic.")
     else:
         with st.spinner("Generating post..."):
             try:
-                result = pipe(prompt, max_new_tokens=length + 50) # Allow room for model excess
-                gen_text = result[0]['generated_text'].strip()
+                generations = pipe(prompt, max_new_tokens=length + 32)
+                result = generations[0]["generated_text"].strip()
                 st.success("Here's your LinkedIn post:")
-                st.write(gen_text)
+                st.write(result)
+                st.download_button("Download Post as Text", result, file_name="linkedin_post.txt")
             except Exception as e:
                 st.error(f"Error generating post: {e}")
 
 st.markdown("---")
-st.write("Upload or edit your own style samples below for future runs.")
-
-uploaded_file = st.file_uploader("Upload style_samples.json (sample LinkedIn posts)", type=["json"])
-if uploaded_file:
+st.write("Upload your own style samples to personalize generation (overwrite previous samples):")
+file = st.file_uploader("Upload style_samples.json", type=["json"])
+if file is not None:
     try:
-        uploaded_samples = json.load(uploaded_file)
-        with open(SAMPLE_FILE, "w") as fout:
-            json.dump(uploaded_samples, fout)
-        st.success(f"Sample file uploaded and saved ({len(uploaded_samples)} samples). Refresh the app to use them.")
+        new_samples = json.load(file)
+        with open("style_samples.json", "w") as f:
+            json.dump(new_samples, f)
+        st.success(f"File uploaded successfully! Now {len(new_samples)} style samples saved. Reload app to use.")
     except Exception as e:
-        st.error(f"Failed to read uploaded file: {e}")
+        st.error(f"Upload failed: {e}")