Update app.py

app.py

@@ -18,100 +18,97 @@ load_dotenv()
 MODEL = "text-embedding-ada-002"
 st.set_page_config(page_title="Visual Embeddings and Similarity", page_icon="🤖", layout="wide")
 
-def main():
-    # sidebar with openai api key and nomic token
-    st.sidebar.title("Credentials")
-    st.sidebar.write("OpenAI API Key")
-    openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key", value=os.getenv("OPENAI_API_KEY"))
-    st.sidebar.write("Nomic Token")
-    nomic_token = st.sidebar.text_input("Enter your Nomic Token", value=os.getenv("NOMIC_TOKEN"))
-
-    openai.api_key = os.getenv("OPENAI_API_KEY")
-    nomic.login(os.getenv("NOMIC_TOKEN"))
-
-    # get data
-    datafile_path = "food_review.csv"
-    # show only columns ProductId, Score, Summary, Text, n_tokens, embedding
-    df = pd.read_csv(datafile_path, usecols=[0,1,3, 5, 7, 8])
-    st.title("Visual Embeddings and Similarity")
-    st.write("Amazon food reviews dataset")
-    st.write(df)
-    
-    st.write("Search similarity")
-    form = st.form('Embeddings')
-    question = form.text_input("Enter a sentence to search for semantic similarity", value="I love this soup")
-    btn = form.form_submit_button("Run")
-
-    if btn:
-        # si openai api key no es none y nomic token no es none
-        if openai_api_key is not None and nomic_token is not None:
-            with st.spinner("Loading"):
-                search_term_vector = get_embedding(question, engine="text-embedding-ada-002")
-                search_term_vector = np.array(search_term_vector)
-
-                matrix = np.array(df.embedding.apply(literal_eval).to_list())
-
-                # Compute distances to the search_term_vector
-                distances = np.linalg.norm(matrix - search_term_vector, axis=1)
-                df['distance_to_search_term'] = distances
-
-                # Normalize the distances to range 0-1 for coloring
-                df['normalized_distance'] = (df['distance_to_search_term'] - df['distance_to_search_term'].min()) / (df['distance_to_search_term'].max() - df['distance_to_search_term'].min())
-
-                # 2D visualization
-                # Create a t-SNE model and transform the data
-                tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)
-                vis_dims = tsne.fit_transform(matrix)
-
-                colors = cm.rainbow(df['normalized_distance'])
-                x = [x for x,y in vis_dims]
-                y = [y for x,y in vis_dims]
-
-                # Plot points with colors corresponding to their distance from search_term_vector
-                plt.scatter(x, y, color=colors, alpha=0.3)
-
-                # Set title and plot
-                plt.title("Similarity to search term visualized in language using t-SNE")
-                
-                
-                # Convert 'embedding' column to numpy arrays
-                df['embedding'] = df['embedding'].apply(lambda x: np.array(literal_eval(x)))
-                df["similarities"] = df['embedding'].apply(lambda x: cosine_similarity(x, search_term_vector))
-                
-                st.title("Visual embedding of the search term and the 20 most similar sentences")
-                #create two columns
-                col1, col2 = st.columns(2)
-                #col1
-                #show st.plot in col1
-                col1.pyplot(plt)
-                
-                #col2
-                #show df in col2, but only the columns, text and similarities
-                col2.write(df[['similarities','Text']].sort_values("similarities", ascending=False).head(20))
-                
-                # Convert to a list of lists of floats
-                st.title("Nomic mappping embeddings")
-                embeddings = np.array(df.embedding.to_list())            
-                df = df.drop('embedding', axis=1)
-                df = df.rename(columns={'Unnamed: 0': 'id'})
-
-                data = df.to_dict('records')
-                project = atlas.map_embeddings(embeddings=embeddings, data=data,
-                                            id_field='id',
-                                            colorable_fields=['Score'])
-                # Convert project to a string before getting link information
-                project_str = str(project)
-
-                st.text(project_str)
-                # Split the project string at the colon and take the second part (index 1)
-                project_link = project_str.split(':', 1)[1]
-
-                # Trim any leading or trailing whitespace
-                project_link = project_link.strip()
-
-                # Crea un iframe con la URL y muéstralo con Streamlit
-                st.markdown(f'<iframe src="{project_link}" width="100%" height="600px"></iframe>', unsafe_allow_html=True)
-        else:
-            st.write("Please enter your OpenAI API Key and Nomic Token in the sidebar")
-if __name__ == "__main__":
-    main()
+# sidebar with openai api key and nomic token
+st.sidebar.title("Credentials")
+st.sidebar.write("OpenAI API Key")
+openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key", value=os.getenv("OPENAI_API_KEY"))
+st.sidebar.write("Nomic Token")
+nomic_token = st.sidebar.text_input("Enter your Nomic Token", value=os.getenv("NOMIC_TOKEN"))
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+nomic.login(os.getenv("NOMIC_TOKEN"))
+
+# get data
+datafile_path = "food_review.csv"
+# show only columns ProductId, Score, Summary, Text, n_tokens, embedding
+df = pd.read_csv(datafile_path, usecols=[0,1,3, 5, 7, 8])
+st.title("Visual Embeddings and Similarity")
+st.write("Amazon food reviews dataset")
+st.write(df)
+
+st.write("Search similarity")
+form = st.form('Embeddings')
+question = form.text_input("Enter a sentence to search for semantic similarity", value="I love this soup")
+btn = form.form_submit_button("Run")
+
+if btn:
+    # si openai api key no es none y nomic token no es none
+    if openai_api_key is not None and nomic_token is not None:
+        with st.spinner("Loading"):
+            search_term_vector = get_embedding(question, engine="text-embedding-ada-002")
+            search_term_vector = np.array(search_term_vector)
+
+            matrix = np.array(df.embedding.apply(literal_eval).to_list())
+
+            # Compute distances to the search_term_vector
+            distances = np.linalg.norm(matrix - search_term_vector, axis=1)
+            df['distance_to_search_term'] = distances
+
+            # Normalize the distances to range 0-1 for coloring
+            df['normalized_distance'] = (df['distance_to_search_term'] - df['distance_to_search_term'].min()) / (df['distance_to_search_term'].max() - df['distance_to_search_term'].min())
+
+            # 2D visualization
+            # Create a t-SNE model and transform the data
+            tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)
+            vis_dims = tsne.fit_transform(matrix)
+
+            colors = cm.rainbow(df['normalized_distance'])
+            x = [x for x,y in vis_dims]
+            y = [y for x,y in vis_dims]
+
+            # Plot points with colors corresponding to their distance from search_term_vector
+            plt.scatter(x, y, color=colors, alpha=0.3)
+
+            # Set title and plot
+            plt.title("Similarity to search term visualized in language using t-SNE")
+            
+            
+            # Convert 'embedding' column to numpy arrays
+            df['embedding'] = df['embedding'].apply(lambda x: np.array(literal_eval(x)))
+            df["similarities"] = df['embedding'].apply(lambda x: cosine_similarity(x, search_term_vector))
+            
+            st.title("Visual embedding of the search term and the 20 most similar sentences")
+            #create two columns
+            col1, col2 = st.columns(2)
+            #col1
+            #show st.plot in col1
+            col1.pyplot(plt)
+            
+            #col2
+            #show df in col2, but only the columns, text and similarities
+            col2.write(df[['similarities','Text']].sort_values("similarities", ascending=False).head(20))
+            
+            # Convert to a list of lists of floats
+            st.title("Nomic mappping embeddings")
+            embeddings = np.array(df.embedding.to_list())            
+            df = df.drop('embedding', axis=1)
+            df = df.rename(columns={'Unnamed: 0': 'id'})
+
+            data = df.to_dict('records')
+            project = atlas.map_embeddings(embeddings=embeddings, data=data,
+                                        id_field='id',
+                                        colorable_fields=['Score'])
+            # Convert project to a string before getting link information
+            project_str = str(project)
+
+            st.text(project_str)
+            # Split the project string at the colon and take the second part (index 1)
+            project_link = project_str.split(':', 1)[1]
+
+            # Trim any leading or trailing whitespace
+            project_link = project_link.strip()
+
+            # Crea un iframe con la URL y muéstralo con Streamlit
+            st.markdown(f'<iframe src="{project_link}" width="100%" height="600px"></iframe>', unsafe_allow_html=True)
+    else:
+        st.write("Please enter your OpenAI API Key and Nomic Token in the sidebar")