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.env

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+DEEPGRAM_API_KEY='43044985b4f715eda085e2b5e974f14edb2abf81'
+OPENAI_API_KEY='9b3fd60a04ff4517be88e57bb91f205f'

Dockerfile

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+# Use the official Python 3.10.9 image
+FROM python:3.10.9
+
+# Copy the current directory contents into the container at .
+COPY . .
+
+# Set the working directory to /
+WORKDIR /
+
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
+
+# Start the FastAPI app on port 7860, the default port expected by Spaces
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import openai
+from dotenv import load_dotenv
+import os
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure OpenAI for Azure
+openai.api_type = "azure"
+openai.api_base = "https://amplifai-openai.openai.azure.com/"
+openai.api_version = "2023-07-01-preview"
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+app = FastAPI()
+
+class Prompt(BaseModel):
+    text: str
+
+class GenerateResponse(BaseModel):
+    text: str
+class GenerateResponse2(BaseModel):
+    text: dict
+    
+#Summarize function
+def summarize_text(text: str):
+    response = openai.Completion.create(
+        engine="AmplifAI-Chat",
+        prompt="The data is a pre-recorded conversation between a call center agent and a customer. Identify the following: keywords/metrics about the conversation, a summary of the conversation including details, points of improvement for the agent, and coaching advice. Output should be in the format Keywords/Metrics: ,\n Summary: , \nPoints of Improvement: ,\nCoaching Advice:. \n Data: " + text,
+        temperature=0.7,
+        max_tokens=750,
+        top_p=1.0,
+        frequency_penalty=0.0,
+        presence_penalty=0.0
+    )
+    if response.choices:
+        summary = response.choices[0].text.strip()
+        return GenerateResponse(text=summary)
+    else:
+        return "No response from the API."
+
+
+# QA Automation function
+def qa_automation(text: str):
+    questions = [
+        "Did the agent verify the identity of the caller clearly? Provide Details.",
+        "Did the agent resolve the Primary reason of the call? Provide Details.",
+        "Was the agent courteous? Provide Details.",
+        # "Did the agent get a promise to pay? Provide Details."
+        "Rate the satisfaction of the customer on a scale of 10. 0 being worse and 10 being best.",
+        "Rate the customer effort on a scale of 0-10. 0 being the most effort spent by the customer and 10 being least effort:",
+        "How could the agent have made the call easier for the customer?"
+    ]
+    answers = []
+    for question in questions:
+        response = openai.Completion.create(
+            engine="AmplifAI-Chat",
+            prompt=f"Question: {question}\nTranscript: {text}\nAnswer:",
+            temperature=0.2,
+            max_tokens=100,
+            top_p=1.0,
+            frequency_penalty=0.0,
+            presence_penalty=0.0,
+            stop=["\n"]
+        )
+        answer = response.choices[0].text.strip()
+        # Split the answer into the main answer (Yes/No) and the details
+        if "Yes" in answer:
+            main_answer, details = answer.split("Yes", 1)
+            main_answer += "Yes"
+        elif "No" in answer:
+            main_answer, details = answer.split("No", 1)
+            main_answer += "No"
+        else:
+            main_answer = answer
+            details = "No additional details provided."
+
+        # Format the answer with the "Comments:" tag in bold and on a new line
+        formatted_answer = f"**{question}** {main_answer}\n\n**Comments:** {details}"
+
+        answers.append(formatted_answer)
+    return GenerateResponse(text = ("\n\n".join(answers)))
+
+
+def check_agent_steps(text: str):
+    categories = {
+        "Introduction": [
+            "Greet the customer politely.",
+            "Identify yourself and your company.",
+            "Verify the customer's identity to ensure confidentiality."
+        ],
+        "Purpose of the Call": [
+            "State the purpose of the call clearly.",
+            "Mention the specific account or debt in question."
+        ],
+        "Account Review": [
+            "Provide details of outstanding debt, the amount, due date, and charges or fees.",
+            "Confirm whether the customer acknowledges the debt."
+        ],
+        "Listen to the Customer": [
+            "Give the customer an opportunity to explain their situation.",
+            "Show empathy and understanding."
+        ],
+        "Payment Discussion": [
+            "Ask the customer about their ability to pay the outstanding amount.",
+            "Discuss payment options and negotiate a payment plan if necessary.",
+            "Set clear terms for the payment plan, including amounts and due dates."
+        ],
+        "Confirmation": [
+            "Confirm the agreed-upon payment plan or next steps.",
+            "Provide details on how the payment can be made (e.g., online, phone, mail)."
+        ],
+        "Documentation": [
+            "Inform the customer that the call is recorded for compliance/training purposes.",
+            "Offer to send a written confirmation of any agreements made during the call."
+        ],
+        "Closing": [
+            "Thank the customer for their time and cooperation.",
+            "Provide a contact number for any further questions or concerns.",
+            "End the call politely."
+        ]
+    }
+
+    # Initialize a dictionary to store the results
+    results = {category: {} for category in categories}
+
+    # Iterate through each category and its questions
+    for category, questions in categories.items():
+        for question in questions:
+            # print("Checking question:", question)
+            response = openai.Completion.create(
+                engine="AmplifAI-Chat",
+                prompt=f"Based on the transcript, did the agent follow the step: {question}? Provide a 'Yes' or 'No' answer.\nTranscript: {text}\nAnswer: ",
+                temperature=0.2,
+                max_tokens=10,
+                top_p=1.0,
+                frequency_penalty=0.0,
+                presence_penalty=0.0,
+                stop=["\n"]
+            )
+            answer = response.choices[0].text.strip()
+            # emoji_answer = '✅' if answer == "Yes" else '❌'
+            # print("Answer:", answer)
+            results[category][question] = answer
+    return GenerateResponse2(text=results)
+
+def custom_qa_automation(text: str, custom_question: str):
+    # Ensure the custom question ends with "Provide Details."
+    if not custom_question.strip().endswith("Provide Details."):
+        custom_question = custom_question.strip() + " Provide Details."
+
+    response = openai.Completion.create(
+        engine="AmplifAI-Chat",
+        prompt=f"Question: {custom_question}\nTranscript: {text}\nAnswer:",
+        temperature=0.2,
+        max_tokens=100,
+        top_p=1.0,
+        frequency_penalty=0.0,
+        presence_penalty=0.0,
+        stop=["\n"]
+    )
+    answer = response.choices[0].text.strip()
+    # Split the answer into the main answer (Yes/No) and the details
+    if "Yes" in answer:
+        main_answer, details = answer.split("Yes", 1)
+        main_answer += "Yes"
+    elif "No" in answer:
+        main_answer, details = answer.split("No", 1)
+        main_answer += "No"
+    else:
+        main_answer = answer
+        details = "No additional details provided."
+
+    # Format the answer with the "Comments:" tag in bold and on a new line
+    formatted_answer = f"**{custom_question}** {main_answer}\n\n**Comments:** {details}"
+
+    return GenerateResponse(text=formatted_answer)
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/", tags=["Home"])
+def api_home():
+    return {'detail': 'Welcome to FastAPI TextGen Tutorial!'}
+
+@app.post("/api/summarize", summary="Generate text from prompt", tags=["Generate"], response_model=GenerateResponse)
+def inference(input_prompt: Prompt):
+    return summarize_text(text=input_prompt.text)
+
+@app.post("/api/qautomation", summary="Generate text from prompt", tags=["Generate"], response_model=GenerateResponse)
+def inference(input_prompt: Prompt):
+    return qa_automation(text=input_prompt.text)
+
+@app.post("/api/verify-call-flow", summary="Generate text from prompt", tags=["Generate"], response_model=GenerateResponse2)
+def inference(input_prompt: Prompt):
+    return check_agent_steps(text=input_prompt.text)
+
+@app.post("/api/custom-qa", summary="Generate text from prompt", tags=["Generate"], response_model=GenerateResponse)
+def inference(input_prompt: Prompt, question: Prompt):
+    return custom_qa_automation(text=input_prompt.text, custom_question=question.text)

requirements.txt

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+fastapi==0.99.1
+uvicorn
+requests
+langchain
+openai==0.28.0
+pillow==10.2.0
+pydantic
+python-dotenv