Create app.py

app.py

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+import streamlit as st
+from langchain_community.llms import HuggingFaceHub
+
+# Models for each task
+SUMMARY_MODEL = "google/flan-t5-small"
+QUESTIONS_MODEL = "tiiuae/falcon-rw-1b"
+KEYWORDS_MODEL = "google/flan-t5-small"
+
+# Function to get LangChain LLM
+def get_llm(model_id):
+    return HuggingFaceHub(
+        repo_id=model_id,
+        model_kwargs={"temperature": 0.5, "max_new_tokens": 150},
+        task="text2text-generation"
+    )
+
+# Streamlit app UI
+st.set_page_config(page_title="🧠 Multi-LLM Research Assistant")
+st.title("🧠 Research Assistant using Multiple LLMs via LangChain")
+
+topic = st.text_input("🔍 Enter your research topic")
+
+if st.button("Run Multi-LLM Analysis"):
+    if not topic.strip():
+        st.warning("Please enter a topic to continue.")
+    else:
+        with st.spinner("Generating..."):
+
+            # Step 1: Summary
+            summary_prompt = f"Provide a short summary about: {topic}"
+            summary_model = get_llm(SUMMARY_MODEL)
+            summary = summary_model.predict(summary_prompt)
+
+            # Step 2: Research Questions
+            questions_prompt = f"Give three research questions about: {topic}"
+            questions_model = get_llm(QUESTIONS_MODEL)
+            questions = questions_model.predict(questions_prompt)
+
+            # Step 3: Keywords
+            keywords_prompt = f"List five keywords related to: {topic}"
+            keywords_model = get_llm(KEYWORDS_MODEL)
+            keywords = keywords_model.predict(keywords_prompt)
+
+        # Display results
+        st.success("✅ Done! Here's your research output:")
+        st.subheader("📄 Summary")
+        st.write(summary)
+
+        st.subheader("❓ Research Questions")
+        st.write(questions)
+
+        st.subheader("🔑 Keywords")
+        st.write(keywords)