Update app.py

app.py

@@ -46,6 +46,71 @@
 # iface.launch()
 
 
+# import torch
+# import pandas as pd
+# from sentence_transformers import SentenceTransformer, util
+# import gradio as gr
+
+# model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
+# df = pd.read_csv("cleaned1.csv")
+# df2 = pd.read_csv("cleaned2.csv")
+# embeddings = torch.load("embeddings1.pt")
+# embeddings2 = torch.load("embeddings2.pt")
+
+# # def search_fatwa(data):
+# #     query = data[0] if data else ""
+# #     query_embedding = model.encode(query, convert_to_tensor=True)
+# #     top_idx = int(util.pytorch_cos_sim(query_embedding, embeddings)[0].argmax())
+# #     top_idx2 = int(util.pytorch_cos_sim(query_embedding, embeddings2)[0].argmax())
+# #     return {
+# #         "question1": df.iloc[top_idx]["question"],
+# #         "link1": df.iloc[top_idx]["link"],
+# #         "question2": df2.iloc[top_idx2]["question"],
+# #         "link2": df2.iloc[top_idx2]["link"]
+# #     }
+
+# def search_fatwa(data):
+#     query = data[0] if isinstance(data, list) else data
+#     if not query:
+#         return {"question1": "", "link1": "", "question2": "", "link2": ""}
+#     query_embedding = model.encode(query, convert_to_tensor=True)
+#     top_idx = int(util.pytorch_cos_sim(query_embedding, embeddings)[0].argmax())
+#     top_idx2 = int(util.pytorch_cos_sim(query_embedding, embeddings2)[0].argmax())
+#     # return {
+#     #     "question1": df.iloc[top_idx]["question"],
+#     #     "link1": df.iloc[top_idx]["link"],
+#     #     "question2": df2.iloc[top_idx2]["question"],
+#     #     "link2": df2.iloc[top_idx2]["link"]
+#     # }
+#     result = f"""Question 1: {df.iloc[top_idx]["question"]}
+#         Link 1: {df.iloc[top_idx]["link"]}
+        
+#         Question 2: {df2.iloc[top_idx2]["question"]}
+#         Link 2: {df2.iloc[top_idx2]["link"]}"""
+#     return result
+
+# iface = gr.Interface(
+#     fn=search_fatwa, 
+#     inputs=[gr.Textbox(label="text", lines=3)], 
+#     outputs="text"  # Changed from "json" to "text"
+# )
+
+# # iface = gr.Interface(fn=search_fatwa, inputs=[gr.Textbox(label="text", lines=3)], outputs="json")
+
+
+
+
+# # iface = gr.Interface(
+# #   fn=predict, 
+# #   inputs=[gr.Textbox(label="text", lines=3)],
+# #   outputs='text',
+# #   title=title,
+# # )
+
+# iface.launch()
+
+
+
 import torch
 import pandas as pd
 from sentence_transformers import SentenceTransformer, util
@@ -57,54 +122,34 @@ df2 = pd.read_csv("cleaned2.csv")
 embeddings = torch.load("embeddings1.pt")
 embeddings2 = torch.load("embeddings2.pt")
 
-# def search_fatwa(data):
-#     query = data[0] if data else ""
-#     query_embedding = model.encode(query, convert_to_tensor=True)
-#     top_idx = int(util.pytorch_cos_sim(query_embedding, embeddings)[0].argmax())
-#     top_idx2 = int(util.pytorch_cos_sim(query_embedding, embeddings2)[0].argmax())
-#     return {
-#         "question1": df.iloc[top_idx]["question"],
-#         "link1": df.iloc[top_idx]["link"],
-#         "question2": df2.iloc[top_idx2]["question"],
-#         "link2": df2.iloc[top_idx2]["link"]
-#     }
-
-def search_fatwa(data):
-    query = data[0] if isinstance(data, list) else data
-    if not query:
-        return {"question1": "", "link1": "", "question2": "", "link2": ""}
+def search_fatwa(query):
+    # Handle both string and list inputs
+    if isinstance(query, list):
+        query = query[0] if query else ""
+    
+    if not query or query.strip() == "":
+        return "No query provided"
+    
     query_embedding = model.encode(query, convert_to_tensor=True)
     top_idx = int(util.pytorch_cos_sim(query_embedding, embeddings)[0].argmax())
     top_idx2 = int(util.pytorch_cos_sim(query_embedding, embeddings2)[0].argmax())
-    # return {
-    #     "question1": df.iloc[top_idx]["question"],
-    #     "link1": df.iloc[top_idx]["link"],
-    #     "question2": df2.iloc[top_idx2]["question"],
-    #     "link2": df2.iloc[top_idx2]["link"]
-    # }
+    
+    # Return formatted text (like your working first app)
     result = f"""Question 1: {df.iloc[top_idx]["question"]}
-        Link 1: {df.iloc[top_idx]["link"]}
-        
-        Question 2: {df2.iloc[top_idx2]["question"]}
-        Link 2: {df2.iloc[top_idx2]["link"]}"""
+Link 1: {df.iloc[top_idx]["link"]}
+
+Question 2: {df2.iloc[top_idx2]["question"]}
+Link 2: {df2.iloc[top_idx2]["link"]}"""
+    
     return result
 
+# Use the same structure as your working first app
 iface = gr.Interface(
     fn=search_fatwa, 
-    inputs=[gr.Textbox(label="text", lines=3)], 
-    outputs="text"  # Changed from "json" to "text"
+    inputs=[gr.Textbox(label="text", lines=3)],
+    outputs='text',  # Changed to 'text' like your working app
+    title="Search CSV"
 )
 
-# iface = gr.Interface(fn=search_fatwa, inputs=[gr.Textbox(label="text", lines=3)], outputs="json")
-
-
-
-
-# iface = gr.Interface(
-#   fn=predict, 
-#   inputs=[gr.Textbox(label="text", lines=3)],
-#   outputs='text',
-#   title=title,
-# )
-
-iface.launch()
+# Enable API access for curl requests
+iface.launch(share=False, show_api=True)