Create app.py

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+import coqui_tts
+from tempfile import NamedTemporaryFile
+
+# Initialize models
+@st.cache_resource
+def load_models():
+    summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
+    summarizer_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
+    question_generator = pipeline("text2text-generation", model="valhalla/t5-small-qg-hl")
+    qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
+    tts = coqui_tts.TTS("tts_models/en/ljspeech/glow-tts")  # Load voice model
+    return summarizer_model, summarizer_tokenizer, question_generator, qa_model, tts
+
+summarizer_model, summarizer_tokenizer, question_generator, qa_model, tts = load_models()
+
+def summarize_document(text):
+    input_ids = summarizer_tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True)
+    summary_ids = summarizer_model.generate(input_ids, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return summarizer_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+def generate_questions(summary):
+    return question_generator(summary)
+
+def generate_audio(text, voice_gender):
+    tts.voice = "male" if voice_gender == "male" else "female"
+    audio_file = NamedTemporaryFile(delete=False, suffix=".wav")
+    tts.tts_to_file(text=text, file_path=audio_file.name)
+    return audio_file.name
+
+def app():
+    st.title("Interactive Document Summarizer")
+    uploaded_file = st.file_uploader("Upload a document", type=["txt", "pdf", "docx"])
+    if uploaded_file:
+        raw_text = uploaded_file.read().decode("utf-8")
+        st.write("Processing document...")
+
+        # Summarize
+        summary = summarize_document(raw_text)
+        st.write("Summary Generated:")
+        st.write(summary)
+
+        # Generate dialogue
+        questions = generate_questions(summary)
+        dialogue = [{"persona": "male", "text": q["generated_text"]} for q in questions]
+        for idx, item in enumerate(dialogue):
+            if idx % 2 == 1:
+                dialogue[idx]["persona"] = "female"
+
+        # Interactive simulation
+        st.write("Simulating Conversation:")
+        for item in dialogue:
+            st.write(f"{item['persona'].capitalize()} says: {item['text']}")
+            audio_path = generate_audio(item["text"], item["persona"])
+            st.audio(audio_path, format="audio/wav")
+
+if __name__ == "__main__":
+    app()