Update app.py

app.py

@@ -93,4 +93,51 @@ def search_documents(query):
     context = "\n".join([match['metadata']['text'] for match in results['matches']])
     
     prompt = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
-    inputs = tokenizer(prompt, return_tens)
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=1024, truncation=True)
+    
+    outputs = model.generate(
+        **inputs,
+        max_length=512,
+        num_beams=4,
+        temperature=0.7,
+        top_p=0.9
+    )
+    
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Format sources
+    sources = [f"Source: {match['metadata']['file_name']}" for match in results['matches']]
+    
+    return answer, "\n".join(sources)
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# PDF Document Search and Q&A")
+    
+    with gr.Tab("Upload Documents"):
+        file_output = gr.File(
+            file_count="multiple",
+            label="Upload PDF Files"
+        )
+        upload_button = gr.Button("Process PDFs")
+        upload_output = gr.Textbox(label="Processing Results")
+    
+    with gr.Tab("Search and Ask"):
+        query_input = gr.Textbox(label="Enter your question")
+        search_button = gr.Button("Search")
+        answer_output = gr.Textbox(label="Answer")
+        sources_output = gr.Textbox(label="Sources")
+    
+    upload_button.click(
+        process_multiple_pdfs,
+        inputs=[file_output],
+        outputs=[upload_output]
+    )
+    
+    search_button.click(
+        search_documents,
+        inputs=[query_input],
+        outputs=[answer_output, sources_output]
+    )
+
+demo.launch()