Create app.py

app.py

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+import streamlit as st
+import json
+from transformers import pipeline
+from huggingface_hub import login
+import os
+
+# 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 = []
+
+# 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
+
+pipe = load_pipeline()
+
+# 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!")
+
+with st.form("post_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")
+
+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"
+
+# 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."
+)
+
+# Results area
+if submit:
+    if not topic.strip():
+        st.warning("Please enter a topic for your post.")
+    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()
+                st.success("Here's your LinkedIn post:")
+                st.write(gen_text)
+            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:
+    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.")
+    except Exception as e:
+        st.error(f"Failed to read uploaded file: {e}")