Update main.py

main.py

@@ -1,232 +1,229 @@
-from flask import Flask, request, jsonify, render_template, Response
-import os
-import requests
-import json
-from scipy import spatial
-from flask_cors import CORS
-import random
-import numpy as np
-from langchain_chroma import Chroma
-from chromadb import Documents, EmbeddingFunction, Embeddings
-
-
-app = Flask(__name__)
-CORS(app)
-
-
-class MyEmbeddingFunction(EmbeddingFunction):
-
-    def embed_documents(self, input: Documents) -> Embeddings:
-        for i in range(5):
-            try:
-                embeddings = []
-                url = "https://api.deepinfra.com/v1/inference/BAAI/bge-large-en-v1.5"
-
-                payload = json.dumps({
-                    "inputs": input
-                })
-                headers = {
-                    'Accept': 'application/json, text/plain, */*',
-                    'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
-                    'Connection': 'keep-alive',
-                    'Content-Type': 'application/json',
-                    'Origin': 'https://deepinfra.com',
-                    'Referer': 'https://deepinfra.com/',
-                    'Sec-Fetch-Dest': 'empty',
-                    'Sec-Fetch-Mode': 'cors',
-                    'Sec-Fetch-Site': 'same-site',
-                    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
-                    'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
-                    'sec-ch-ua-mobile': '?0',
-                    'sec-ch-ua-platform': '"Windows"'
-                }
-
-                response = requests.request("POST", url, headers=headers, data=payload)
-                return response.json()["embeddings"]
-            except:
-                pass
-
-    def embed_query(self, input: Documents) -> Embeddings:
-        print(input)
-        for i in range(5):
-            try:
-                embeddings = []
-                url = "https://api.deepinfra.com/v1/inference/BAAI/bge-large-en-v1.5"
-
-                payload = json.dumps({
-                    "inputs": [input]
-                })
-                headers = {
-                    'Accept': 'application/json, text/plain, */*',
-                    'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
-                    'Connection': 'keep-alive',
-                    'Content-Type': 'application/json',
-                    'Origin': 'https://deepinfra.com',
-                    'Referer': 'https://deepinfra.com/',
-                    'Sec-Fetch-Dest': 'empty',
-                    'Sec-Fetch-Mode': 'cors',
-                    'Sec-Fetch-Site': 'same-site',
-                    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
-                    'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
-                    'sec-ch-ua-mobile': '?0',
-                    'sec-ch-ua-platform': '"Windows"'
-                }
-
-                response = requests.request("POST", url, headers=headers, data=payload)
-                return response.json()["embeddings"][0]
-            except:
-                pass
-
-CHROMA_PATH = "chroma"
-custom_embeddings = MyEmbeddingFunction()
-db = Chroma(
-    persist_directory=CHROMA_PATH, embedding_function=custom_embeddings
-)
-
-
-def embeddingGen(query):
-    url = "https://api.deepinfra.com/v1/inference/BAAI/bge-large-en-v1.5"
-
-    payload = json.dumps({
-        "inputs": [query]
-    })
-    headers = {
-        'Accept': 'application/json, text/plain, */*',
-        'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
-        'Connection': 'keep-alive',
-        'Content-Type': 'application/json',
-        'Origin': 'https://deepinfra.com',
-        'Referer': 'https://deepinfra.com/',
-        'Sec-Fetch-Dest': 'empty',
-        'Sec-Fetch-Mode': 'cors',
-        'Sec-Fetch-Site': 'same-site',
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
-        'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
-        'sec-ch-ua-mobile': '?0',
-        'sec-ch-ua-platform': '"Windows"'
-    }
-
-    response = requests.request("POST", url, headers=headers, data=payload)
-    return response.json()
-
-
-def strings_ranked_by_relatedness(query, df, top_n=5):
-    def relatedness_fn(x, y):
-        x_norm = np.linalg.norm(x)
-        y_norm = np.linalg.norm(y)
-        return np.dot(x, y) / (x_norm * y_norm)
-
-    query_embedding_response = embeddingGen(query)
-    query_embedding = query_embedding_response["embeddings"][0]
-    strings_and_relatednesses = [
-        (row["text"], relatedness_fn(query_embedding, row["embedding"])) for row in df
-    ]
-    strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)
-    strings, relatednesses = zip(*strings_and_relatednesses)
-    return strings[:top_n], relatednesses[:top_n]
-
-
-@app.route("/api/gpt", methods=["POST"])
-def gptRes():
-    data = request.get_json()
-    messages = data["messages"]
-    def inference():
-        url = "https://api.deepinfra.com/v1/openai/chat/completions"
-
-        payload = json.dumps({
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "messages": messages,
-            "stream": True,
-            "max_tokens": 1024,
-        })
-        headers = {
-            'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
-            'Connection': 'keep-alive',
-            'Content-Type': 'application/json',
-            'Origin': 'https://deepinfra.com',
-            'Referer': 'https://deepinfra.com/',
-            'Sec-Fetch-Dest': 'empty',
-            'Sec-Fetch-Mode': 'cors',
-            'Sec-Fetch-Site': 'same-site',
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
-            'X-Deepinfra-Source': 'web-page',
-            'accept': 'text/event-stream',
-            'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"Windows"'
-        }
-
-        response = requests.request("POST", url, headers=headers, data=payload, stream=True)
-
-        for line in response.iter_lines(decode_unicode=True):
-            if line:
-                # try:
-                #     line = line.split("data:")[1]
-                #     line = json.loads(line)
-                #     yield line["choices"][0]["delta"]["content"]
-                # except:
-                #     yield ""
-                yield line
-
-    return Response(inference(), content_type='text/event-stream')
-
-
-@app.route("/", methods=["GET"])
-def index():
-    return render_template("index.html")
-
-
-@app.route("/api/getAPI", methods=["POST"])
-def getAPI():
-    return jsonify({"API":  random.choice(apiKeys)})
-
-
-@app.route("/api/getContext", methods=["POST"])
-def getContext():
-    global db
-    question = request.form["question"]
-    results = db.similarity_search_with_score(question, k=5)
-    context = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
-    sources = [doc.metadata.get("id", None) for doc, _score in results]
-    return jsonify({"context": context, "sources": sources})
-
-
-@app.route("/api/audioGenerate", methods=["POST"])
-def audioGenerate():
-    answer = request.form["answer"]
-    audio = []
-    for i in answer.split("\n"):
-        url = "https://deepgram.com/api/ttsAudioGeneration"
-
-        payload = json.dumps({
-            "text": i,
-            "model": "aura-asteria-en",
-            "demoType": "landing-page",
-            "params": "tag=landingpage-product-texttospeech"
-        })
-        headers = {
-            'accept': '*/*',
-            'accept-language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
-            'content-type': 'application/json',
-            'origin': 'https://deepgram.com',
-            'priority': 'u=1, i',
-            'referer': 'https://deepgram.com/',
-            'sec-ch-ua': '"Not/A)Brand";v="8", "Chromium";v="126", "Google Chrome";v="126"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"Windows"',
-            'sec-fetch-dest': 'empty',
-            'sec-fetch-mode': 'cors',
-            'sec-fetch-site': 'same-origin',
-            'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36'
-        }
-
-        response = requests.request("POST", url, headers=headers, data=payload)
-        audio.append(response.json()["data"])
-    return jsonify({"audio": audio})
-
-
-if __name__ == "__main__":
-    # app.run(debug=True)
-    from waitress import serve
-
-    serve(app, host="0.0.0.0", port=7860)
+from flask import Flask, request, jsonify, render_template, Response
+import os
+import requests
+import json
+from scipy import spatial
+from flask_cors import CORS
+import random
+import numpy as np
+from langchain_chroma import Chroma
+from chromadb import Documents, EmbeddingFunction, Embeddings
+
+app = Flask(__name__)
+CORS(app)
+
+class MyEmbeddingFunction(EmbeddingFunction):
+    def embed_documents(self, input: Documents) -> Embeddings:
+        for i in range(5):
+            try:
+                embeddings = []
+                url = "https://api.deepinfra.com/v1/inference/BAAI/bge-large-en-v1.5"
+
+                payload = json.dumps({
+                    "inputs": input
+                })
+                headers = {
+                    'Accept': 'application/json, text/plain, */*',
+                    'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
+                    'Connection': 'keep-alive',
+                    'Content-Type': 'application/json',
+                    'Origin': 'https://deepinfra.com',
+                    'Referer': 'https://deepinfra.com/',
+                    'Sec-Fetch-Dest': 'empty',
+                    'Sec-Fetch-Mode': 'cors',
+                    'Sec-Fetch-Site': 'same-site',
+                    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
+                    'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
+                    'sec-ch-ua-mobile': '?0',
+                    'sec-ch-ua-platform': '"Windows"'
+                }
+
+                response = requests.request("POST", url, headers=headers, data=payload)
+                return response.json()["embeddings"]
+            except:
+                pass
+
+    def embed_query(self, input: Documents) -> Embeddings:
+        print(input)
+        for i in range(5):
+            try:
+                embeddings = []
+                url = "https://api.deepinfra.com/v1/inference/BAAI/bge-large-en-v1.5"
+
+                payload = json.dumps({
+                    "inputs": [input]
+                })
+                headers = {
+                    'Accept': 'application/json, text/plain, */*',
+                    'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
+                    'Connection': 'keep-alive',
+                    'Content-Type': 'application/json',
+                    'Origin': 'https://deepinfra.com',
+                    'Referer': 'https://deepinfra.com/',
+                    'Sec-Fetch-Dest': 'empty',
+                    'Sec-Fetch-Mode': 'cors',
+                    'Sec-Fetch-Site': 'same-site',
+                    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
+                    'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
+                    'sec-ch-ua-mobile': '?0',
+                    'sec-ch-ua-platform': '"Windows"'
+                }
+
+                response = requests.request("POST", url, headers=headers, data=payload)
+                return response.json()["embeddings"][0]
+            except:
+                pass
+
+CHROMA_PATH = "chroma"
+custom_embeddings = MyEmbeddingFunction()
+db = Chroma(
+    persist_directory=CHROMA_PATH, embedding_function=custom_embeddings
+)
+
+
+def embeddingGen(query):
+    url = "https://api.deepinfra.com/v1/inference/BAAI/bge-large-en-v1.5"
+
+    payload = json.dumps({
+        "inputs": [query]
+    })
+    headers = {
+        'Accept': 'application/json, text/plain, */*',
+        'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
+        'Connection': 'keep-alive',
+        'Content-Type': 'application/json',
+        'Origin': 'https://deepinfra.com',
+        'Referer': 'https://deepinfra.com/',
+        'Sec-Fetch-Dest': 'empty',
+        'Sec-Fetch-Mode': 'cors',
+        'Sec-Fetch-Site': 'same-site',
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
+        'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
+        'sec-ch-ua-mobile': '?0',
+        'sec-ch-ua-platform': '"Windows"'
+    }
+
+    response = requests.request("POST", url, headers=headers, data=payload)
+    return response.json()
+
+
+def strings_ranked_by_relatedness(query, df, top_n=5):
+    def relatedness_fn(x, y):
+        x_norm = np.linalg.norm(x)
+        y_norm = np.linalg.norm(y)
+        return np.dot(x, y) / (x_norm * y_norm)
+
+    query_embedding_response = embeddingGen(query)
+    query_embedding = query_embedding_response["embeddings"][0]
+    strings_and_relatednesses = [
+        (row["text"], relatedness_fn(query_embedding, row["embedding"])) for row in df
+    ]
+    strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)
+    strings, relatednesses = zip(*strings_and_relatednesses)
+    return strings[:top_n], relatednesses[:top_n]
+
+
+@app.route("/api/gpt", methods=["POST"])
+def gptRes():
+    data = request.get_json()
+    messages = data["messages"]
+    def inference():
+        url = "https://api.deepinfra.com/v1/openai/chat/completions"
+
+        payload = json.dumps({
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "messages": messages,
+            "stream": True,
+            "max_tokens": 1024,
+        })
+        headers = {
+            'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
+            'Connection': 'keep-alive',
+            'Content-Type': 'application/json',
+            'Origin': 'https://deepinfra.com',
+            'Referer': 'https://deepinfra.com/',
+            'Sec-Fetch-Dest': 'empty',
+            'Sec-Fetch-Mode': 'cors',
+            'Sec-Fetch-Site': 'same-site',
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
+            'X-Deepinfra-Source': 'web-page',
+            'accept': 'text/event-stream',
+            'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
+            'sec-ch-ua-mobile': '?0',
+            'sec-ch-ua-platform': '"Windows"'
+        }
+
+        response = requests.request("POST", url, headers=headers, data=payload, stream=True)
+
+        for line in response.iter_lines(decode_unicode=True):
+            if line:
+                # try:
+                #     line = line.split("data:")[1]
+                #     line = json.loads(line)
+                #     yield line["choices"][0]["delta"]["content"]
+                # except:
+                #     yield ""
+                yield line
+
+    return Response(inference(), content_type='text/event-stream')
+
+
+@app.route("/", methods=["GET"])
+def index():
+    return render_template("index.html")
+
+
+@app.route("/api/getAPI", methods=["POST"])
+def getAPI():
+    return jsonify({"API":  random.choice(apiKeys)})
+
+
+@app.route("/api/getContext", methods=["POST"])
+def getContext():
+    global db
+    question = request.form["question"]
+    results = db.similarity_search_with_score(question, k=5)
+    context = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
+    sources = [doc.metadata.get("id", None) for doc, _score in results]
+    return jsonify({"context": context, "sources": sources})
+
+
+@app.route("/api/audioGenerate", methods=["POST"])
+def audioGenerate():
+    answer = request.form["answer"]
+    audio = []
+    for i in answer.split("\n"):
+        url = "https://deepgram.com/api/ttsAudioGeneration"
+
+        payload = json.dumps({
+            "text": i,
+            "model": "aura-asteria-en",
+            "demoType": "landing-page",
+            "params": "tag=landingpage-product-texttospeech"
+        })
+        headers = {
+            'accept': '*/*',
+            'accept-language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
+            'content-type': 'application/json',
+            'origin': 'https://deepgram.com',
+            'priority': 'u=1, i',
+            'referer': 'https://deepgram.com/',
+            'sec-ch-ua': '"Not/A)Brand";v="8", "Chromium";v="126", "Google Chrome";v="126"',
+            'sec-ch-ua-mobile': '?0',
+            'sec-ch-ua-platform': '"Windows"',
+            'sec-fetch-dest': 'empty',
+            'sec-fetch-mode': 'cors',
+            'sec-fetch-site': 'same-origin',
+            'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36'
+        }
+
+        response = requests.request("POST", url, headers=headers, data=payload)
+        audio.append(response.json()["data"])
+    return jsonify({"audio": audio})
+
+
+if __name__ == "__main__":
+    # app.run(debug=True)
+    from waitress import serve
+
+    serve(app, host="0.0.0.0", port=7860)