trial app

app.py

@@ -1,86 +1,86 @@
 import gradio as gr
 
-import openai
-import pinecone
+# import openai
+# import pinecone
 import requests
 
-openai.api_key = "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"
-
-pinecone.init(
-    api_key="d307ef27-f3ee-4335-b89d-c866395df920",
-    environment="us-east1-gcp",
-)
-
-limit = 5000
-# 3750
-
-embed_model = "text-embedding-ada-002"
-
-index_name = 'gen-qa'
-index = pinecone.Index(index_name)
-
-# retrieve relevant answers
-def retrieve(query):
-    res = openai.Embedding.create(
-        input=[query],
-        engine=embed_model,
-    )
-
-    # retrieve from Pinecone
-    xq = res['data'][0]['embedding']
-
-    # get relevant contexts
-    res = index.query(xq, top_k=3, include_metadata=True)
-    contexts = [
-        x['metadata']['text'] for x in res['matches']
-    ]
-
-    # build our prompt with the retrieved contexts included
-    prompt_start = (
-        "Answer the question based on the context below.\n\n"+
-        "Context:\n"
-    )
-    prompt_end = (
-        f"\n\nQuestion: {query}\nAnswer:"
-    )
-
-    # append contexts until hitting limit
-    for i in range(1, len(contexts)):
-        if len("\n\n---\n\n".join(contexts[:i])) >= limit:
-            prompt = (
-                prompt_start +
-                "\n\n---\n\n".join(contexts[:i-1]) +
-                prompt_end
-            )
-            break
-        elif i == len(contexts)-1:
-            prompt = (
-                prompt_start +
-                "\n\n---\n\n".join(contexts) +
-                prompt_end
-            )
-    return prompt
-
-# then we complete the context-infused query
-def complete(prompt):
-    # query text-davinci-003
-    res = openai.Completion.create(
-        engine='text-davinci-003',
-        prompt=prompt,
-        temperature=0,
-        max_tokens=500,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0,
-        stop=None
-    )
-    return res['choices'][0]['text'].strip()
+# openai.api_key = "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"
+
+# pinecone.init(
+#     api_key="d307ef27-f3ee-4335-b89d-c866395df920",
+#     environment="us-east1-gcp",
+# )
+
+# limit = 5000
+# # 3750
+
+# embed_model = "text-embedding-ada-002"
+
+# index_name = 'gen-qa'
+# index = pinecone.Index(index_name)
+
+# # retrieve relevant answers
+# def retrieve(query):
+#     res = openai.Embedding.create(
+#         input=[query],
+#         engine=embed_model,
+#     )
+
+#     # retrieve from Pinecone
+#     xq = res['data'][0]['embedding']
+
+#     # get relevant contexts
+#     res = index.query(xq, top_k=3, include_metadata=True)
+#     contexts = [
+#         x['metadata']['text'] for x in res['matches']
+#     ]
+
+#     # build our prompt with the retrieved contexts included
+#     prompt_start = (
+#         "Answer the question based on the context below.\n\n"+
+#         "Context:\n"
+#     )
+#     prompt_end = (
+#         f"\n\nQuestion: {query}\nAnswer:"
+#     )
+
+#     # append contexts until hitting limit
+#     for i in range(1, len(contexts)):
+#         if len("\n\n---\n\n".join(contexts[:i])) >= limit:
+#             prompt = (
+#                 prompt_start +
+#                 "\n\n---\n\n".join(contexts[:i-1]) +
+#                 prompt_end
+#             )
+#             break
+#         elif i == len(contexts)-1:
+#             prompt = (
+#                 prompt_start +
+#                 "\n\n---\n\n".join(contexts) +
+#                 prompt_end
+#             )
+#     return prompt
+
+# # then we complete the context-infused query
+# def complete(prompt):
+#     # query text-davinci-003
+#     res = openai.Completion.create(
+#         engine='text-davinci-003',
+#         prompt=prompt,
+#         temperature=0,
+#         max_tokens=500,
+#         top_p=1,
+#         frequency_penalty=0,
+#         presence_penalty=0,
+#         stop=None
+#     )
+#     return res['choices'][0]['text'].strip()
 
 def greet(query):
     # first we retrieve relevant items from Pinecone
-    query_with_contexts = retrieve(query)
-    # return only the main answer
-    result = complete(query_with_contexts)
+    # query_with_contexts = retrieve(query)
+    # # return only the main answer
+    # result = complete(query_with_contexts)
     response = requests.post("https://siddh4rth-narrify.hf.space/run/predict", json={
         "data": [
             query,