Create app.py

app.py

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+import streamlit as st
+import openai  # or use HuggingFace's transformers or your own LLM
+
+# Set your OpenAI API key
+openai.api_key = st.secrets.get("OPENAI_API_KEY")  # Use Hugging Face secrets
+
+# Helper function to call LLM
+def generate_document(prompt):
+    response = openai.ChatCompletion.create(
+        model="gpt-4",  # Or gpt-3.5-turbo
+        messages=[
+            {"role": "system", "content": "You are an expert product manager and software architect."},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0.7,
+        max_tokens=1500
+    )
+    return response['choices'][0]['message']['content']
+
+# UI
+st.title("🚀 Product Idea to Documentation Generator")
+st.write("Enter a product idea and get a PRD, frontend/backend architecture, and user flow.")
+
+product_idea = st.text_area("💡 Your Product Idea", height=200)
+
+if st.button("Generate Documents"):
+    if not product_idea.strip():
+        st.warning("Please enter a product idea.")
+    else:
+        with st.spinner("Generating PRD..."):
+            prd = generate_document(f"Write a detailed PRD for this product idea:\n{product_idea}")
+        
+        with st.spinner("Generating Frontend Architecture..."):
+            frontend = generate_document(f"Describe the frontend tech stack and architecture for this product:\n{product_idea}")
+        
+        with st.spinner("Generating Backend Architecture..."):
+            backend = generate_document(f"Describe the backend system design for this product:\n{product_idea}")
+        
+        with st.spinner("Generating User Flow..."):
+            user_flow = generate_document(f"Create a simple user flow description for this product:\n{product_idea}")
+
+        # Show results
+        st.subheader("📄 Product Requirements Document (PRD)")
+        st.markdown(prd)
+
+        st.subheader("🧱 Frontend Architecture")
+        st.markdown(frontend)
+
+        st.subheader("🛠 Backend Architecture")
+        st.markdown(backend)
+
+        st.subheader("🧭 User Flow")
+        st.markdown(user_flow)
+
+        # Download options
+        all_text = f"# PRD\n\n{prd}\n\n# Frontend\n\n{frontend}\n\n# Backend\n\n{backend}\n\n# User Flow\n\n{user_flow}"
+        st.download_button("📥 Download as .txt", all_text, file_name="product_docs.txt")