first trial app

app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Function to process the uploaded file and find top 5 matching notes
+def find_matching_notes(uploaded_file, user_input):
+    # Read the uploaded CSV file
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file.name)
+        
+        # Ensure the necessary columns are present
+        if not {'Source', 'Section', 'Notes'}.issubset(df.columns):
+            return "The uploaded file must contain 'Source', 'Section', and 'Notes' columns."
+        
+        # Combine 'Notes' and 'Section' for processing
+        df['Combined'] = df['Notes'] + ' ' + df['Section']
+        
+        # Create TF-IDF vectorizer and transform the texts
+        vectorizer = TfidfVectorizer()
+        all_texts = df['Combined'].tolist() + [user_input]
+        tfidf_matrix = vectorizer.fit_transform(all_texts)
+        
+        # Compute cosine similarity
+        cosine_similarities = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1])
+        
+        # Get the top 5 indices of the most similar entries
+        top_indices = cosine_similarities[0].argsort()[-5:][::-1]
+        
+        # Prepare the results
+        results = df.iloc[top_indices][['Notes', 'Source', 'Section']]
+        results_list = results.values.tolist()
+        
+        return results_list
+    return "Please upload a valid CSV file."
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=find_matching_notes,
+    inputs=[
+        gr.File(label="Upload Research Notes (CSV)"),
+        gr.Textbox(label="Enter your text here", placeholder="Type your content...")
+    ],
+    outputs=gr.Dataframe(headers=["Notes", "Source", "Section"], label="Top 5 Matching Entries"),
+    title="Research Notes Matcher",
+    description="Upload a CSV file with 'Source', 'Section', and 'Notes'. Enter your text to find the top 5 matching notes."
+)
+
+# Launch the app
+iface.launch()