Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import pipeline
+import tempfile
+
+# Set the Streamlit page config
+st.set_page_config(page_title="Meeting Summarizer", layout="centered")
+
+# Title
+st.title("📝 Intelligent Meeting Summarizer")
+st.write("Upload your English meeting audio, and we'll generate a professional summary for you using Hugging Face models.")
+
+# Load ASR pipeline
+@st.cache_resource
+def load_asr_pipeline():
+    return pipeline("automatic-speech-recognition", model="facebook/s2t-medium-librispeech-asr")
+
+# Load Text Generation pipeline
+@st.cache_resource
+def load_summary_pipeline():
+    return pipeline(
+        task="text-generation",
+        model="huggyllama/llama-7b",
+        torch_dtype=torch.float16,
+        device=0  # set to -1 for CPU
+    )
+
+asr_pipeline = load_asr_pipeline()
+gen_pipeline = load_summary_pipeline()
+
+# Upload audio file
+uploaded_file = st.file_uploader("📤 Upload your meeting audio (.wav)", type=["wav", "mp3", "flac"])
+
+if uploaded_file is not None:
+    # Save to temp file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_audio:
+        tmp_audio.write(uploaded_file.read())
+        tmp_audio_path = tmp_audio.name
+
+    st.audio(uploaded_file, format='audio/wav')
+
+    if st.button("🔍 Transcribe and Summarize"):
+        # ASR: Audio to Text
+        with st.spinner("Transcribing audio..."):
+            result = asr_pipeline(tmp_audio_path)
+            transcription = result["text"]
+            st.subheader("📄 Transcribed Text")
+            st.write(transcription)
+
+        # Text to Text
+        with st.spinner("Generating summary..."):
+            prompt = f"Summarize the following meeting transcript into a professional meeting report:\n{transcription}\n\nSummary:"
+            summary = gen_pipeline(prompt, max_new_tokens=300, do_sample=True, top_k=50, temperature=0.7)[0]["generated_text"]
+            st.subheader("🧠 Meeting Summary")
+            st.write(summary)