Update app.py

app.py

@@ -1,79 +1,2 @@
-import streamlit as st
-import json
-import os
 from transformers import pipeline
-
-def load_style_samples(filename="style_samples.json"):
-    if os.path.exists(filename):
-        with open(filename, "r") as f:
-            return json.load(f)
-    return []
-
-@st.cache_resource(show_spinner=False)
-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
-    )
-
-# Load model and style samples
-pipe = load_text_gen_pipeline()
-style_samples = load_style_samples()
-
-st.set_page_config(page_title="LinkedIn Post Generator", layout="centered")
-st.title("🔗 LinkedIn Post Generator (Hugging Face Space)")
-st.write("Generate LinkedIn posts with custom style and parameters.")
-
-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("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")
-
-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"
-
-prompt = (
-    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."
-)
-
-if submitted:
-    if not topic.strip():
-        st.warning("Please enter a topic.")
-    else:
-        with st.spinner("Generating post..."):
-            try:
-                generations = pipe(prompt, max_new_tokens=length + 32)
-                result = generations[0]["generated_text"].strip()
-                st.success("Here's your LinkedIn post:")
-                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 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:
-        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"Upload failed: {e}")
+print(pipeline('sentiment-analysis')('Test'))