Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,27 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+from langchain_google_genai import ChatGoogleGenerativeAI
+from dotenv import load_dotenv
 import streamlit as st
+from langchain_core.prompts import PromptTemplate,load_prompt
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+load_dotenv()
+
+model = ChatGoogleGenerativeAI(model="gemini-2.0-flash")
+
+st.header('Reasearch Tool')
+
+paper_input = st.selectbox( "Select Research Paper Name", ["Attention Is All You Need", "BERT: Pre-training of Deep Bidirectional Transformers", "GPT-3: Language Models are Few-Shot Learners", "Diffusion Models Beat GANs on Image Synthesis"] )
+
+style_input = st.selectbox( "Select Explanation Style", ["Beginner-Friendly", "Technical", "Code-Oriented", "Mathematical"] ) 
+
+length_input = st.selectbox( "Select Explanation Length", ["Short (1-2 paragraphs)", "Medium (3-5 paragraphs)", "Long (detailed explanation)"] )
+
+template = load_prompt('template.json')
+
+if st.button('Summarize'):
+    chain = template | model
+    result = chain.invoke({
+        'paper_input':paper_input,
+        'style_input':style_input,
+        'length_input':length_input
+    })
+    st.write(result.content)