Upload app.py

app.py

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+# import gradio as gr
+# import httpx
+# import json
+# from typing import Tuple, Any
+
+# # Define the FastAPI endpoint URL
+# FASTAPI_ENDPOINT = "http://localhost:8000/websearch"
+
+
+# def query_api(query: str) -> Tuple[Any, Any]:
+#     try:
+#         # Send POST request to FastAPI endpoint with streaming enabled
+#         with httpx.Client() as client:
+#             with client.stream("POST", FASTAPI_ENDPOINT, json={"query": query}, timeout=60.0) as response:
+#                 response.raise_for_status()  # Raise an exception for 4xx or 5xx status codes
+                
+#                 # Process the streaming response
+#                 response_data = ""
+#                 for chunk in response.iter_text():
+#                     response_data += chunk
+                
+#                 # Parse the accumulated response data as JSON
+#                 response_json = json.loads(response_data)
+                
+#                 # Extract content and citations from the response JSON
+#                 content = response_json.get("content", "")
+#                 citations = response_json.get("citations", [])
+                
+#                 # Beautify content using Markdown formatting
+#                 beautified_content = f"# Search Results\n\n{content}"
+                
+#                 # Beautify citations by adding Markdown links
+#                 beautified_citations = "# Citations\n\n"
+#                 for i, citation in enumerate(citations, start=1):
+#                     beautified_citations += f"{i}. [{citation}]({citation})\n"
+                
+#                 # Yield the beautified content and citations
+#                 yield beautified_content, beautified_citations
+#     except httpx.TimeoutException:
+#         yield "Request timed out. Please try again later.", ""
+#     except httpx.HTTPStatusError as e:
+#         yield f"HTTP error occurred: {e}", ""
+#     except Exception as e:
+#         yield f"An error occurred: {e}", ""
+
+
+# # Create Gradio interface
+# with gr.Blocks(css=".gradio-container { background-color: #f5f5f5; padding: 20px; border-radius: 10px; }") as demo:
+#     gr.Markdown("# Web Search Application")
+    
+#     with gr.Row():
+#         with gr.Column():
+#             query = gr.Textbox(
+#                 label="Enter your query",
+#                 placeholder="Type your search query here...",
+#                 lines=2,
+#                 max_lines=4,
+#                 value="",
+#                 elem_id="query-input"
+#             )
+#             submit_button = gr.Button("Search")
+        
+#         with gr.Column():
+#             output_content = gr.Textbox(
+#                 label="Response Content",
+#                 placeholder="Search results will appear here...",
+#                 lines=10,
+#                 max_lines=20,
+#                 value="",
+#                 elem_id="response-content"
+#             )
+#             output_citations = gr.Textbox(
+#                 label="Citations",
+#                 placeholder="Citations will appear here...",
+#                 lines=5,
+#                 max_lines=10,
+#                 value="",
+#                 elem_id="response-citations"
+#             )
+    
+#     # Set up event listener
+#     submit_button.click(query_api, inputs=query, outputs=[output_content, output_citations])
+    
+#     gr.Markdown("Powered by FastAPI and Gradio")
+
+# # Launch the Gradio application
+# demo.launch()
+
+
+import gradio as gr
+import httpx
+import json
+
+# Define the FastAPI endpoint URL
+FASTAPI_ENDPOINT = "http://localhost:8000/websearch"
+
+def query_api(query: str) -> tuple:
+    try:
+        # Send POST request to FastAPI endpoint with streaming enabled
+        with httpx.Client() as client:
+            with client.stream("POST", FASTAPI_ENDPOINT, json={"query": query}, timeout=60.0) as response:
+                response.raise_for_status()  # Raise an exception for 4xx or 5xx status codes
+                
+                # Process the streaming response
+                response_data = ""
+                for chunk in response.iter_text():
+                    response_data += chunk
+                
+                # Parse the accumulated response data as JSON
+                response_json = json.loads(response_data)
+                
+                # Extract content and citations from the response JSON
+                content = response_json.get("content", "")
+                citations = response_json.get("citations", [])
+                
+                # Beautify content using Markdown formatting
+                beautified_content = f"# Search Results\n\n{content}"
+                
+                # Beautify citations by adding Markdown links
+                beautified_citations = "# Citations/Sources\n\n"
+                for i, citation in enumerate(citations, start=1):
+                    beautified_citations += f"{i}. [{citation}]({citation})\n"
+                
+                # Yield the beautified content and citations
+                yield beautified_content, beautified_citations
+    except httpx.TimeoutException:
+        yield "# Request Timeout\n\nRequest timed out. Please try again later.", ""
+    except httpx.HTTPStatusError as e:
+        yield f"# HTTP Error\n\nHTTP error occurred: {e}", ""
+    except Exception as e:
+        yield f"# Error\n\nAn error occurred: {e}", ""
+
+# Create Gradio interface
+with gr.Blocks(css=".gradio-container { background-color: #f5f5f5; padding: 20px; border-radius: 10px; }", theme=gr.themes.Citrus()) as demo:
+    gr.Markdown("# Web Search Application")
+    
+    with gr.Row():
+        with gr.Column(
+            render=True,
+            show_progress=True
+        ):
+            query = gr.Textbox(
+                label="Enter your query",
+                placeholder="Type your search query here...",
+                lines=2,
+                max_lines=4,
+                value="",
+                elem_id="query-input"
+            )
+            submit_button = gr.Button("Search")
+        
+        with gr.Column(
+            render=True,
+            show_progress=True
+        ):
+            output_content = gr.Markdown(
+                label="Response Content",
+                value="",
+                elem_id="response-content",
+                height="600px",
+                visible=True,
+                show_label=True
+                
+            )
+            output_citations = gr.Markdown(
+                label="Citations",
+                value="",
+                elem_id="response-citations",
+                height="200px",
+                visible=True,
+                show_label=True
+            )
+    
+    # Set up event listener
+    submit_button.click(query_api, inputs=query, outputs=[output_content, output_citations])
+    
+    gr.Markdown("Powered by FastAPI and Gradio")
+
+# Launch the Gradio application
+demo.launch()